Evidence map›Paper›PMID 39342515›Full record

ArticleDiscover oncology2024

AI-powered genomic mutation signature for predicting immune checkpoint inhibitor therapy outcomes in gastroesophageal cancer: a multi-cohort analysis.

Bingyin Yang, Cuie Cheng, Jingfang Zhou, Haoxiang Ni, Haoran Liu, Yiwei Fu, Rui Li

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PubMed Full text at PMC Publisher
In one paragraph

Article in Discover oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

7 authors.

Bingyin Yang *Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Cuie Cheng *Department of Gastroenterology, Affiliated Changshu Hospital of Nantong University, Suzhou, Jiangsu, China.
Jingfang Zhou *Department of Gastroenterology, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
Haoxiang NiDepartment of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Haoran LiuDepartment of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Yiwei FuDepartment of Gastroenterology, Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou, Jiangsu, China. yiweifu@sina.com.
Rui LiDepartment of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China. lrhcsz@163.com.

Funding

Research support for this study was provided by the multi-center clinical research project of major illnesses in Soochow DZXYJ202301
6 · The paper itself

Abstract

backgroundImmune checkpoint inhibitors (ICIs) have significantly transformed the treatment of gastroesophageal cancer (GEC). However, the lack of reliable prognostic biomarkers hinders the ability to predict patient response to ICI therapy.

methodsIn this study, we engineered and validated a genomic mutation signature (GMS) utilizing an innovative artificial intelligence (AI) algorithm to forecast ICI therapy outcomes in GEC patients. We further explored immune profiles across subtypes through comprehensive multiomics analysis. Our investigation of drug sensitivity data from the Genomics of Drug Sensitivity in Cancer (GDSC) database led to the identification of trametinib as a potential therapeutic agent. We subsequently evaluated trametinib's efficacy in AGS and MKN45 cell lines using Cell Counting Kit-8 (CCK8) assays and clonogenic experiments.

resultsWe developed a GMS by integrating 297 algorithms, enabling autonomous prognosis prediction for GEC patients. The GMS demonstrated consistent performance across three public cohorts, exhibiting high sensitivity and specificity for overall survival (OS) at 6, 12, and 18 months, as shown by Receiver Operator Characteristic Curve (ROC) analysis. Notably, the GMS surpassed traditional clinical and molecular features, including tumor mutational burden (TMB), programmed death-ligand 1 (PD-L1) expression, and microsatellite instability (MSI), in predictive accuracy. Low-risk samples exhibited elevated levels of cytolytic immune cells and heightened immunogenic potential compared to high-risk samples. Our investigation identified trametinib as a potential therapeutic agent. An inverse correlation was observed between GMS and trametinib IC50. Moreover, the high-risk-derived AGS cell line showed increased sensitivity to trametinib compared to the low-risk-derived MKN45 cell line.

conclusionThe GMS utilized in this study successfully demonstrated the ability to reliably predict the survival advantage for patients with GECs undergoing ICI therapy.

Indexed as

Artificial intelligenceGastrointestinal cancerGenomic mutationImmune landscapeImmunotherapy

Identifiers

PMID39342515
PMCPMC11439860

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