Evidence map›Paper›PMID 40912256›Full record

ArticleCell reports. Medicine2025

Large language models enable tumor-type classification and localization of cancers of unknown primary from genomic data.

Jilei Liu, Meng Yang, Yajing Bi, Junqing Zhang, Yichen Yang, Yang Li, Hongru Shen, Kexin Chen, Xiangchun Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports. Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
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

9 authors.

Jilei LiuKey Laboratory of Cancer Prevention and Therapy, Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China.
Meng YangKey Laboratory of Cancer Prevention and Therapy, Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China.
Yajing BiKey Laboratory of Cancer Prevention and Therapy, Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China.
Junqing ZhangKey Laboratory of Cancer Prevention and Therapy, Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China.
Yichen YangKey Laboratory of Cancer Prevention and Therapy, Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China.
Yang LiKey Laboratory of Cancer Prevention and Therapy, Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China.
Hongru ShenKey Laboratory of Cancer Prevention and Therapy, Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China.
Kexin ChenDepartment of Epidemiology and Biostatistics, Key Laboratory of Prevention and Control of Major Diseases in the Population Ministry of Education, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China. Electronic address: chenkexin@tmu.edu.cn.
Xiangchun LiKey Laboratory of Cancer Prevention and Therapy, Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China. Electronic address: lixiangchun@tmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumor-type classification is critical for effective cancer treatment, yet current methods based on genomic alterations lack flexibility and have limited performance. Here, we introduce OncoChat, an artificial intelligence (AI) model designed to classify 69 tumor types by integrating diverse genomic alterations. Developed on genomic data from 158,836 tumors sequenced with targeted cancer gene panels, OncoChat demonstrates superior performance, achieving a micro-averaged precision-recall area under the curve (PRAUC) of 0.810 (95% confidence interval [CI], 0.803-0.816), accuracy of 0.774, and an F1 score of 0.756, outperforming baseline methods. In a cancer of unknown primary (CUP) dataset of 26 cases whose types were subsequently confirmed, OncoChat correctly identified 22 cases. In two larger CUP datasets (n = 719 and 158), tumor types predicted by OncoChat were associated with survival outcomes and mutation profiles consistent with those of known tumor types. OncoChat offers promising potential for clinical decision support, particularly in managing patients with CUP.

Indexed as

GenomicsNeoplasms, Unknown PrimaryArtificial IntelligenceHumansLarge Language ModelsMutationartificial intelligencecancer of unknown primarylarge language modeltumor-type classification

Identifiers

PMID40912256
PMCPMC12490231

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.