Medium levels of transcription and replication related chromosomal instability are associated with poor clinical outcome.
Algorithms
Antineoplastic Agents
/ administration & dosage
Chromosomal Instability
Chromosomes
/ ultrastructure
DNA
/ analysis
DNA Damage
DNA Mutational Analysis
DNA Repair
DNA Replication
Enhancer Elements, Genetic
Gene Regulatory Networks
Genome, Human
Humans
Kaplan-Meier Estimate
Neoplasm Metastasis
Neoplasms
/ genetics
Promoter Regions, Genetic
Risk
Sarcoma
/ pathology
Sequence Analysis, DNA
Transcription, Genetic
Treatment Outcome
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
06 12 2021
06 12 2021
Historique:
received:
22
06
2021
accepted:
08
11
2021
entrez:
7
12
2021
pubmed:
8
12
2021
medline:
28
1
2022
Statut:
epublish
Résumé
Genomic instability (GI) influences treatment efficacy and resistance, and an accurate measure of it is lacking. Current measures of GI are based on counts of specific structural variation (SV) and mutational signatures. Here, we present a holistic approach to measuring GI based on the quantification of the steady-state equilibrium between DNA damage and repair as assessed by the residual breakpoints (BP) remaining after repair, irrespective of SV type. We use the notion of Hscore, a BP "hotspotness" magnitude scale, to measure the propensity of genomic structural or functional DNA elements to break more than expected by chance. We then derived new measures of transcription- and replication-associated GI that we call iTRAC (transcription-associated chromosomal instability index) and iRACIN (replication-associated chromosomal instability index). We show that iTRAC and iRACIN are predictive of metastatic relapse in Leiomyosarcoma (LMS) and that they may be combined to form a new classifier called MAGIC (mixed transcription- and replication-associated genomic instability classifier). MAGIC outperforms the gold standards FNCLCC and CINSARC in stratifying metastatic risk in LMS. Furthermore, iTRAC stratifies chemotherapeutic response in LMS. We finally show that this approach is applicable to other cancers.
Identifiants
pubmed: 34873180
doi: 10.1038/s41598-021-02787-x
pii: 10.1038/s41598-021-02787-x
pmc: PMC8648741
doi:
Substances chimiques
Antineoplastic Agents
0
DNA
9007-49-2
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
23429Informations de copyright
© 2021. The Author(s).
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