Evidence map›Paper›PMID 39026719›Full record

ArticlebioRxiv : the preprint server for biology2024

Tumor Subtype Classification Tool for HPV-associated Head and Neck Cancers.

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

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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, Michigan, USA.
Bailey F GarbDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, USA.
Tingting QinDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, USA.
Sarah SoppeDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, USA.
Elizabeth LopezDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Snehal PatilDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Nisha J D'SilvaDepartment of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, Michigan, USA.
Laura S RozekDepartment of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Maureen A SartorDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, USA.

Funding

Strategic Vision & Impact on Environmental HealthP30ES017885 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Dana Dolinoy · 2011 to 2026
$21.3M
Strategies to Overcome Immune Resistance in Head and Neck CancersP01CA240239 · NCI · YALE UNIVERSITY · PI SARTOR, MAUREEN AGNES · 2019 to 2023
$8.4M
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 P01 CA240239NCI NIH HHS R01 CA250214NCI NIH HHS T32 CA140044NIEHS NIH HHS P30 ES017885
6 · The paper itself

Abstract

Importance: Molecular subtypes of HPV-associated Head and Neck Squamous Cell Carcinoma (HNSCC), named IMU (immune strong) and KRT (highly keratinized), are well-recognized and have been shown to have distinct mechanisms of carcinogenesis, 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. Objective: To develop a robust, accurate machine learning-based classification tool that standardizes the process of subtyping HPV+ HNSCC, and to investigate the clinical, demographic, and molecular features associated with subtype in a meta-analysis of four patient cohorts. Data Sources: We conducted RNA-seq on 67 HNSCC FFPE blocks from University of Michigan hospital. Combining this with three publicly available datasets, we utilized a total of 229 HPV+ HNSCC RNA-seq samples. All participants were HPV+ according to RNA expression. An ensemble machine learning approach with five algorithms and three different input training gene sets were developed, with final subtype determined by majority vote. Several additional steps were taken to ensure rigor and reproducibility throughout. Study Selection: The classifier was trained and tested using 84 subtype-labeled HPV+ RNA-seq samples from two cohorts: University of Michigan (UM; n=18) and TCGA-HNC (n=66). The classifier robustness was validated with two independent cohorts: 83 samples from the HPV Virome Consortium and 62 additional samples from UM. We revealed 24 of 39 tested clinicodemographic and molecular variables significantly associated with subtype. Results: The classifier achieved 100% accuracy in the test set. Validation on two additional cohorts demonstrated successful separation by known features of the subtypes. Investigating the relationship between subtype and 39 molecular and clinicodemographic variables revealed IMU is associated with epithelial-mesenchymal transition (p=2.25×10 Conclusions and Relevance: This study provides a reliable classifier for subtyping HPV+ HNSCC tumors as either IMU or KRT based on bulk RNA-seq data, and additionally, improves our understanding of the HPV+ HNSCC subtypes.

Identifiers

PMID39026719
PMCPMC11257489

What OpenQuestion holds

Textmetadata
LicenceCC BY-ND
Read underepoch 390

Registered trials

None linked

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.