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Metadata

Name
Combination of HX008 And Niraparib in GErm-line-mutAted Metastatic Breast Cancer
Repository
ClinicalTrials.gov
Identifier
clinicaltrials:NCT04508803
Description
A number of anti-PD-1/L1 monoclonal antibodies have been approved for the treatment of
various advanced tumors in the world, and many studies on anti-PD-1 /L1 monoclonal antibodies
for breast cancer are also being carried out. HX008 (Taizhou Hanzhong Biomedical Co.,
Ltd.China) combined gemcitabine and cisplatin (GP) regimen for first-line treatment of
advanced triple negative breast cancer has been shown good efficacy. On the other hand,HRD as
the target of PARP inhibitor therapy in the treatment of breast cancer has a broad prospect,
In HRD tumor patients, the use of PARPi can make obstacles of DNA damage repair(DDR),
accumulation of DNA damage, thus promote the apoptosis of tumor cells. Several PARPi have
been approved worldwide (including Olaparib, Rucaparib, Niraparib, Talazoparib, Veliparib)
for the treatment of ovarian and/or breast cancer. Theoretically, PARPi and anti-PD-1
monoclonal antibody can play a synergistic mechanism. In this study, HX008 combined with
Niraparib is designed to treat metastatic breast cancer patients with DDR gene (BRCA1/2,
PALB2, CHEK2, ATM, ATR, BAP1, BARD1, BLM, BRIP1, CHEK1, CDK12, FANCA, FANCC, FANCD2, FANCE,
FANCF, FANCM, MRE11A, NBN, PTEN, RAD50, RAD51C, RAD51D, WRN) pathogenic/suspected pathogenic
germline mutation, so as to explore the possibility of more combined therapy for breast
cancer to achieve better therapeutic effect.
Data or Study Types
clinical trial
Keywords
PARP Inhibitors, Breast Cancer, Anti-PD-1 Monoclonal Antibodies, Homologous Recombination Deficiency
Source Organization
Unknown
Access Conditions
available
Year
2020
Access Hyperlink
https://clinicaltrials.gov/ct2/show/NCT04508803

Distributions

  • Encoding Format: HTML ; URL: https://clinicaltrials.gov/ct2/show/results/NCT04508803
This project was funded in part by grant U24AI117966 from the NIH National Institute of Allergy and Infectious Diseases as part of the Big Data to Knowledge program. We thank all members of the bioCADDIE community for their valuable input on the overall project.