Evidence map›Paper›PMID 41545500›Full record

ArticleNPJ precision oncology2026

Spatial transcriptomics reveals molecular heterogeneity and subtype-specific therapeutic targets in small cell lung cancer.

Tongji Xie, Le Tang, Guangyu Fan, Xiaohong Han, Yuankai Shi

Abstract read
In one paragraph

Article in NPJ precision oncology, 2026. 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

5 authors.

Tongji Xie *Department of Medical Oncology, Beijing Key Laboratory of Key Technologies for Early Clinical Trial Evaluation of Innovative Drugs for Major Diseases, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Le Tang *Department of Medical Oncology, Beijing Key Laboratory of Key Technologies for Early Clinical Trial Evaluation of Innovative Drugs for Major Diseases, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Guangyu Fan *Department of Medical Oncology, Beijing Key Laboratory of Key Technologies for Early Clinical Trial Evaluation of Innovative Drugs for Major Diseases, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Xiaohong HanClinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Key Technologies for Early Clinical Trial Evaluation of Innovative Drugs for Major Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China. hanxiaohong@pumch.cn.
Yuankai ShiDepartment of Medical Oncology, Beijing Key Laboratory of Key Technologies for Early Clinical Trial Evaluation of Innovative Drugs for Major Diseases, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China. syuankai@cicams.ac.cn.

Funding

National Science and Technology Major Project for Key New Drug Development 2017ZX09304015
6 · The paper itself

Abstract

Small cell lung cancer (SCLC) is a highly aggressive malignancy with strong associations to smoking, characterized by initial platinum sensitivity followed by rapid recurrence and poor long-term survival. The evolutionary processes driving this high plasticity and intratumoral heterogeneity remain inadequately understood, hampering the development of effective therapies. In this study, we established a comprehensive spatial transcriptomic (ST) landscape of SCLC. Our approach integrated two key methodological innovations: the Edgeindex metric for the quantitative assessment of tumor spatial architecture, and a specialized artificial neural network (ANN) model for precise tumor annotation. Utilizing this analytical framework, we systematically resolved SCLC heterogeneity across clinical, spatial, functional, and temporal dimensions. Furthermore, pathway enrichment analysis was performed to explore the underlying molecular mechanisms. This work provides a multi-dimensional resource for deciphering the complexity of SCLC.

Identifiers

PMID41545500
PMCPMC12949244

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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.