Protocol for using scCURE to construct an immunotherapy outcome prediction model.

Yujun Liu, Xin Zou, Henry H Y Tong, Yuan Ji, Lianjiang Tan, Jie Hao

Abstract read

In one paragraph

Article in STAR protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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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.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yujun LiuDepartment of Radiation Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.

Xin ZouNinth People's Hospital, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, National Clinical Research Center of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China; Digital Diagnosis and Treatment Innovation Center for Cancer, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China. Electronic address: xzou@fudan.edu.cn.

Henry H Y TongCentre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao, China.

Yuan JiMolecular Pathology Center, Department Pathology, Zhongshan Hospital, Fudan University, Shanghai, China.

Lianjiang TanSchool of Materials Science and Engineering, Shanghai Institute of Technology, Shanghai 201418, China. Electronic address: tanlianjiang@126.com.

Jie HaoInstitute of Clinical Science, Zhongshan Hospital, Fudan University, Shanghai 200032, China. Electronic address: jhao@fudan.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is challenging to predict the immunotherapy outcome from the baseline status of patients with cancer. Here, we introduce a protocol for constructing an immunotherapy prediction model utilizing single-cell RNA sequencing (scRNA-seq)-based changed and unchanged cell recognition during immunotherapy (scCURE). Initially, we describe the steps for using scCURE to discriminate unchanged cells with similar cellular and molecular functions from whole scRNA-seq data. Subsequently, we demonstrate ways to construct immunotherapy outcome prediction models using either scRNA-seq or bulk RNA sequencing (RNA-seq) data, predicated on the unchanged cells identified by scCURE. For complete details on the use and execution of this protocol, please refer to Zou et al.

Indexed as

ImmunotherapyNeoplasmsHumansSequence Analysis, RNASingle-Cell AnalysisTreatment OutcomeBioinformaticsCancerHealth Sciences

Identifiers

PMID39661506

DOI10.1016/j.xpro.2024.103476

PMCPMC11683223

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LicenceCC BY-NC-ND

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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.