Evidence map›Paper›PMID 40804292›Full record

ArticleNPJ precision oncology2025

PharmaFormer predicts clinical drug responses through transfer learning guided by patient derived organoid.

Yuru Zhou, Quanhui Dai, Yanming Xu, Shuang Wu, Minzhang Cheng, Bing Zhao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

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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

6 authors.

Yuru ZhouSchool of Basic Medical Sciences, Institute of Biomedical Innovation, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Quanhui DaiZ Lab, bioGenous BIOTECH, Shanghai, China.
Yanming XuInstitute of Organoid Technology, Kunming Medical University, Kunming, China.
Shuang WuZ Lab, bioGenous BIOTECH, Shanghai, China.
Minzhang ChengSchool of Basic Medical Sciences, Institute of Biomedical Innovation, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China. mzcheng@ncu.edu.cn.
Bing ZhaoSchool of Basic Medical Sciences, Institute of Biomedical Innovation, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China. bingzhao@ncu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A major challenge in effective cancer treatment is the variability of drug responses among patients. Patient-derived organoids greatly preserve the genetic and histological characteristics even the drug sensitivities of primary tumor tissues, therefore provide a compelling approach to predict clinical outcome. However, the individual organoid culture and following drug response test are time and cost-consuming, which hinders the potential clinical application. Here, we developed PharmaFormer, a clinical drug response prediction model based on custom Transformer architecture and transfer learning. PharmaFormer was initially pre-trained with the abundant gene expression and drug sensitivity data of 2D cell lines, and was then finalized through a model further fine-tuned with the limited organoid pharmacogenomic data accumulated at the present stage. Our results demonstrate that PharmaFormer, integrating both pan-cancer cell lines and organoids of a specific type of tumor, provides a dramatically improved accurate prediction of clinical drug response. This study highlights that advanced AI models combined with biomimetic organoid models will accelerate precision medicine and future drug development.

Identifiers

PMID40804292
PMCPMC12350944

What Socratic holds

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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 Socratic graph.