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dc.contributor.authorTovey, H
dc.contributor.authorCheang, MCU
dc.date.accessioned2019-12-13T14:06:15Z
dc.date.issued2019-11-25
dc.identifier.citationCancers, 2019, 11 (12)
dc.identifier.issn2072-6694
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/3469
dc.identifier.eissn2072-6694
dc.identifier.doi10.3390/cancers11121864
dc.description.abstractThe concept of precision medicine has been around for many years and recent advances in high-throughput sequencing techniques are enabling this to become reality. Within the field of breast cancer, a number of signatures have been developed to molecularly sub-classify tumours. Notable examples recently approved by National Institute for Health and Care Excellence in the UK to guide treatment decisions for oestrogen receptors (ER)+ human epidermal growth factor receptor 2 (HER2)- patients include Prosigna test, EndoPredict, and Oncotype DX. However, a population of still unmet need are those with triple negative breast cancer (TNBC). Accounting for 15-20% of patients, this population has comparatively poor prognosis and as yet no targeted treatment options. Studies have shown that some patients with TNBC respond favourably to DNA damaging drugs (carboplatin) or agents which inhibit DNA damage response (poly ADP ribose polymerase (PARP) inhibitors). Known to be a heterogeneous population, there is a need to identify further TNBC patients who may benefit from these treatments. A number of signatures have been identified based on association with treatment response or specific genetic features/pathways however many of these were not restricted to TNBC patients and as of yet are not common practice in the clinic.
dc.formatElectronic
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleIdentifying Biomarkers to Pair with Targeting Treatments within Triple Negative Breast Cancer for Improved Patient Stratification.
dc.typeJournal Article
dcterms.dateAccepted2019-11-18
rioxxterms.versionofrecord10.3390/cancers11121864
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2019-11-25
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfCancers
pubs.issue12
pubs.notesNot known
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies/Clinical Trials & Statistics Unit
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies/Genomic Analysis – Clinical Trials
pubs.organisational-group/ICR/Students
pubs.organisational-group/ICR/Students/PhD and MPhil
pubs.organisational-group/ICR/Students/PhD and MPhil/18/19 Starting Cohort
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies/Clinical Trials & Statistics Unit
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies/Genomic Analysis – Clinical Trials
pubs.organisational-group/ICR/Students
pubs.organisational-group/ICR/Students/PhD and MPhil
pubs.organisational-group/ICR/Students/PhD and MPhil/18/19 Starting Cohort
pubs.publication-statusPublished
pubs.volume11
pubs.embargo.termsNot known
icr.researchteamClinical Trials & Statistics Uniten_US
icr.researchteamGenomic Analysis – Clinical Trialsen_US
dc.contributor.icrauthorTovey, Hollyen
dc.contributor.icrauthorCheang, Chonen


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