Evidence map›Paper›PMID 41045845›Full record

ArticleOral oncology2025

Tumor subtype classification tool for HPV-associated head and neck cancers.

Shiting Li, Bailey F Garb, Tingting Qin, Sarah E Soppe, Elizabeth Lopez, Snehal Patil, Nisha J D'Silva, Laura S Rozek, Maureen A Sartor

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PubMed Full text at PMC Publisher
In one paragraph

Article in Oral oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Shiting LiDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
Bailey F GarbDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
Tingting QinDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
Sarah E SoppeDepartment of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Elizabeth LopezDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
Snehal PatilDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Nisha J D'SilvaDepartment of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI, USA; Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA.
Laura S RozekDepartment of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA. Electronic address: lr898@georgetown.edu.
Maureen A SartorDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA; Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA. Electronic address: sartorma@umich.edu.

Funding

Proteogenomics of Cancer Training ProgramT32CA140044 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI RAO, ARVIND, SARTOR, MAUREEN AGNES · 2010 to 2024
$3.9M
Downstream effects of HPV integration on survival/metastasis in oropharyngeal cancerR01CA250214 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI D'SILVA, NISHA J, ROZEK, LAURA · 2020 to 2024
$3.1M
NCI NIH HHS R01 CA250214NCI NIH HHS T32 CA140044
6 · The paper itself

Abstract

backgroundMolecular subtypes of HPV-associated Head and Neck Squamous Cell Carcinoma (HNSCC), named IMU (immune strong) and KRT (highly keratinized), are well-recognized due to distinct molecular features, tumor microenvironments, clinical outcomes, and potentially differing optimal treatment strategies. Currently, no standardized method exists to subtype a new HPV + HNSCC tumor. Our paper introduces a machine learning-based classifier and webtool to reliably subtype HPV + HNSCC tumors using the IMU/KRT paradigm and highlights the importance of subtype in HPV + HNSCC.

methodsWe conducted RNA-seq on 67 HNSCC tumors from University of Michigan Health. Combining this with three publicly available datasets, we utilized a total of 229 HPV + HNSCC RNA-seq samples. The classifier was trained and tested using 84 subtype-labeled HPV + RNA-seq samples and validated with the remaining samples. We also tested the association of 37 clinicodemographic and molecular variables with subtype.

resultsThe classifier achieved 100% accuracy in the test set. Validation on two additional cohorts demonstrated successful separation by known features of the subtypes. Investigation of the relationship between subtype and the molecular and clinicodemographic variables revealed 21 significant associations, both confirming previous findings and revealing novel subtype associations.

conclusionsThis study provides a reliable classifier for subtyping HPV + HNSCC tumors as either IMU or KRT based on bulk RNA-seq data and improves our understanding of the HPV + HNSCC subtypes.

Indexed as

Head and Neck NeoplasmsPapillomavirus InfectionsSquamous Cell Carcinoma of Head and NeckAgedFemaleHumansMachine LearningMaleMiddle AgedPapillomaviridaeRNA-SeqHNSCCHPVMachine LearningRecurrenceRNA-seqTumor subtype

Identifiers

PMID41045845
PMCPMC13274521

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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.