Evidence map›Paper›PMID 40769146›Full record

ArticleCell reports. Medicine2025

Computational framework for prioritizing candidate compounds overcoming the resistance of pancancer immunotherapy.

Fangyoumin Feng, Tian He, Ping Lin, Jinwu Hu, Bihan Shen, Zhixuan Tang, Jian Zhou, Jia Fan, Bo Hu, Hong 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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Fangyoumin FengShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Tian HeDepartment of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, and Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai 20032, China.
Ping LinShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Jinwu HuDepartment of Liver Cancer, Shanghai Geriatric Medical Center, Shanghai 20032, China.
Bihan ShenShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Zhixuan TangShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Jian ZhouDepartment of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, and Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai 20032, China.
Jia FanDepartment of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, and Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai 20032, China.
Bo HuDepartment of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, and Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai 20032, China. Electronic address: hu.bo@zs-hospital.sh.cn.
Hong LiShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China. Electronic address: lihong01@sinh.ac.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Combination therapy has emerged as an effective approach to overcome resistance to immunotherapy. However, only a small number of drugs have been identified with synergistic effects with immunotherapy. Here, we develop a computational framework (IGeS-BS) to recommend compounds that potentially overcome resistance to immunotherapy. A meta-analysis of approximately 1,000 transcriptomes from immunotherapy patients revealed 33 tumor microenvironment (TME) signatures that can robustly and accurately estimate immunotherapy responses. An immuno-boosting landscape for more than 10,000 compounds and 13 cancer types was subsequently generated on The Cancer Genome Atlas (TCGA) and The Library of Integrated Network-Based Cellular Signatures (LINCS) datasets. Furthermore, the immuno-boosting effects of several high-scoring compounds were evaluated by in vitro and in vivo experiments in hepatocellular carcinoma and other cancer types. The results showed that the two best compounds (SB-366791 and CGP-60474) significantly alleviate the resistance of hepatocellular carcinoma to anti-PD1 therapy by activating immune cells. Collectively, our research provides an efficient framework for discovering compounds that enhance immunotherapy responses.

Indexed as

Computational BiologyDrug Resistance, NeoplasmImmunotherapyLiver NeoplasmsNeoplasmsAnimalsCarcinoma, HepatocellularCell Line, TumorHumansMiceTumor Microenvironmentdrughepatocellular carcinomaimmuno-boosting scoreimmunotherapypancancer

Identifiers

PMID40769146
PMCPMC12432370

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Registered trials

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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.