Evidence map›Paper›PMID 38335222›Full record

ArticlePLoS computational biology2024

An Epigenomic fingerprint of human cancers by landscape interrogation of super enhancers at the constituent level.

Xiang Liu, Nancy Gillis, Chang Jiang, Anthony McCofie, Timothy I Shaw, Aik-Choon Tan, Bo Zhao, Lixin Wan, Derek R Duckett, Mingxiang Teng

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2024. 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, top 98% of its field
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, 0 citations in OpenAlex.

  1. Review
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

10 authors at 3 institutions in 1 country.

Xiang LiuDepartment of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida, United States of America.ORCID 0000-0002-7081-3694
Nancy GillisDepartment of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida, United States of America.ORCID 0000-0002-7744-8490
Chang JiangDepartment of Molecular Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America.
Anthony McCofieDepartment of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida, United States of America.
Timothy I ShawDepartment of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida, United States of America.
Aik-Choon TanDepartment of Oncological Sciences, Huntsman Cancer Institute, The University of Utah, Salt Lake City, Utah, United States of America.
Bo ZhaoDivision of Infectious Disease, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.
Lixin WanDepartment of Molecular Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America.
Derek R DuckettDepartment of Drug Discovery, Moffitt Cancer Center, Tampa, Florida, United States of America.
Mingxiang TengDepartment of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida, United States of America.ORCID 0000-0002-8536-8941
Moffitt Cancer Center · USBrigham and Women's Hospital · USUniversity of Utah · US

Funding

TRANSLATIONAL RESEARCHP30CA076292 · NCI · UNIVERSITY OF SOUTH FLORIDA · PI Christine O'Connell · 1998 to 2026
$93.5M
Targeting Epstein-Barr Virus super-enhancersR01AI123420 · NIAID · BRIGHAM AND WOMEN'S HOSPITAL · PI Bo Zhao · 2016 to 2026
$5.1M
Developing CDK12 inhibitors to overcome therapy resistance in HER2+ and KRAS driven breast and lung cancersR01CA262530 · NCI · H. LEE MOFFITT CANCER CTR & RES INST · PI DUCKETT, DEREK RONALD, HAURA, ERIC B. · 2021 to 2025
$3.5M
Characterizing oncogenic function of ITCH in melanomaR01CA255398 · NCI · H. LEE MOFFITT CANCER CTR & RES INST · PI Lixin Wan · 2021 to 2026
$1.8M
Integrative analysis to identify genomic biomarkers in HPV positive oral cancerR03DE030580 · NIDCR · H. LEE MOFFITT CANCER CTR & RES INST · PI TENG, MINGXIANG · 2023 to 2024
$337k
NCI NIH HHS P30 CA076292NCI NIH HHS R01 CA255398NCI NIH HHS R01 CA262530NIDCR NIH HHS R03 DE030580
6 · The paper itself

Abstract

Super enhancers (SE), large genomic elements that activate transcription and drive cell identity, have been found with cancer-specific gene regulation in human cancers. Recent studies reported the importance of understanding the cooperation and function of SE internal components, i.e., the constituent enhancers (CE). However, there are no pan-cancer studies to identify cancer-specific SE signatures at the constituent level. Here, by revisiting pan-cancer SE activities with H3K27Ac ChIP-seq datasets, we report fingerprint SE signatures for 28 cancer types in the NCI-60 cell panel. We implement a mixture model to discriminate active CEs from inactive CEs by taking into consideration ChIP-seq variabilities between cancer samples and across CEs. We demonstrate that the model-based estimation of CE states provides improved functional interpretation of SE-associated regulation. We identify cancer-specific CEs by balancing their active prevalence with their capability of encoding cancer type identities. We further demonstrate that cancer-specific CEs have the strongest per-base enhancer activities in independent enhancer sequencing assays, suggesting their importance in understanding critical SE signatures. We summarize fingerprint SEs based on the cancer-specific statuses of their component CEs and build an easy-to-use R package to facilitate the query, exploration, and visualization of fingerprint SEs across cancers.

Indexed as

NeoplasmsSuper EnhancersEnhancer Elements, GeneticEpigenomicsGene Expression RegulationHumans

Identifiers

PMID38335222
PMCPMC10883583
OpenAlexW4391677541

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.