Evidence map›Paper›PMID 40164718›Full record

ArticleLeukemia2025

Discrete genetic subtypes and tumor microenvironment signatures correlate with peripheral T-cell lymphoma outcomes.

Yasuhito Suehara, Kana Sakamoto, Manabu Fujisawa, Kota Fukumoto, Yoshiaki Abe, Kenichi Makishima, Sakurako Suma, Tatsuhiro Sakamoto, Keiichiro Hattori, Takeshi Sugio and 13 more

Abstract read
In one paragraph

Article in Leukemia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
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  3. Review
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4 · The record

Corrections and comments

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.

Yasuhito SueharaDepartment of Hematology, University of Tsukuba Hospital, Tsukuba, Japan.
Kana SakamotoPathology Project for Molecular Targets, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan.
Manabu FujisawaDepartment of Hematology, Institute of Medicine, University of Tsukuba, Tsukuba, Japan.ORCID 0009-0007-1122-6549
Kota FukumotoDepartment of Hematology, University of Tsukuba Hospital, Tsukuba, Japan.
Yoshiaki AbeDepartment of Hematology, University of Tsukuba Hospital, Tsukuba, Japan.ORCID 0000-0002-1021-7911
Kenichi MakishimaDepartment of Hematology, University of Tsukuba Hospital, Tsukuba, Japan.ORCID 0000-0002-5808-816X
Sakurako SumaDepartment of Hematology, University of Tsukuba Hospital, Tsukuba, Japan.
Tatsuhiro SakamotoDepartment of Hematology, University of Tsukuba Hospital, Tsukuba, Japan.ORCID 0000-0001-6852-0721
Keiichiro HattoriDepartment of Hematology, University of Tsukuba Hospital, Tsukuba, Japan.
Takeshi SugioDepartment of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Science, Fukuoka, Japan.
Koji KatoDepartment of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Science, Fukuoka, Japan.
Koichi AkashiDepartment of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Science, Fukuoka, Japan.ORCID 0000-0001-7763-5723
Kosei MatsueDivision of Hematology/Oncology, Department of Internal Medicine, Kameda Medical Center, Kamogawa, Japan.
Kentaro NaritaPathology Project for Molecular Targets, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan.
Kengo TakeuchiPathology Project for Molecular Targets, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan.
Joaquim CarrerasDepartment of Pathology, Tokai University School of Medicine, Isehara, Japan.ORCID 0000-0002-6129-8299
Naoya NakamuraDepartment of Pathology, Tokai University School of Medicine, Isehara, Japan.
Kenichi ChibaDepartment of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan.
Yuichi ShiraishiDepartment of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan.
Satoru MiyanoM&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan.ORCID 0000-0002-1753-6616
Seishi OgawaDepartment of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan.
Shigeru ChibaDepartment of Hematology, University of Tsukuba Hospital, Tsukuba, Japan.
Mamiko Sakata-YanagimotoDepartment of Hematology, University of Tsukuba Hospital, Tsukuba, Japan. sakatama@md.tsukuba.ac.jp.ORCID 0000-0001-7310-8045

Funding

Japan Agency for Medical Research and Development (AMED) JP22ck0106544Japan Agency for Medical Research and Development (AMED) JP23ck0106797Japan Agency for Medical Research and Development (AMED) JP23tk0124002Japan Agency for Medical Research and Development (AMED) JP24ck0106908Leukemia and Lymphoma Society (Leukemia & Lymphoma Society) 3442-25MEXT | Japan Science and Technology Agency (JST) JP22zf0127009MEXT | Japan Society for the Promotion of Science (JSPS) JP19K23879MEXT | Japan Society for the Promotion of Science (JSPS) JP20J20851MEXT | Japan Society for the Promotion of Science (JSPS) JP21H02945MEXT | Japan Society for the Promotion of Science (JSPS) JP21K16261MEXT | Japan Society for the Promotion of Science (JSPS) JP21K16262MEXT | Japan Society for the Promotion of Science (JSPS) JP23K15293MEXT | Japan Society for the Promotion of Science (JSPS) JP23K15316MEXT | Japan Society for the Promotion of Science (JSPS) JP23K15317
6 · The paper itself

Abstract

Peripheral T-cell lymphoma (PTCL) exhibits a diverse clinical spectrum, necessitating methods to categorize patients based on genomic abnormalities or tumor microenvironment (TME) profiles. We conducted an integrative multiomics study in 129 PTCL patients, performing whole-exome sequencing and identifying three genetic subtypes: C1, C2, and C3. C2 was characterized by loss of tumor suppressor genes and chromosomal instability, while C1 and C3 shared T follicular helper (TFH)-related genomic alterations, with C3 also showing a high incidence of IDH2 mutations and chromosome 5 gain. Compared to C1, survival was significantly worse in C2 (HR 2.52; 95% CI, 1.37-4.63) and C3 (HR 2.14; 95% CI, 1.17-3.89). We also estimated the proportions of immune cell fractions from the bulk RNA sequencing data using CIBERSORTx and classified TME signatures into the following hierarchical clusters: TME1 (characterized by increased B and TFH cells), TME2 (macrophages), and TME3 (activated mast cells). TME2 was associated with shorter survival (HR 3.4; 95% CI, 1.6-7.5) and was more frequent in C2 (64.3%) than in C1 (7.7%), whereas C1 had more TME3 signatures (80.8% vs. 28.6%). These findings highlight a significant relationship between genetic subtypes and TME signatures in PTCL, with important implications for clinical prognosis.

Indexed as

Biomarkers, TumorLymphoma, T-Cell, PeripheralTumor MicroenvironmentAdultAgedExome SequencingFemaleHumansMaleMiddle AgedMutationPrognosisBiomarkers, Tumor

Identifiers

PMID40164718
PMCPMC12055585

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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.