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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...

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Related Experiment Video

Updated: Jun 27, 2026

Visualizing DNA Damage Repair Proteins in Patient-Derived Ovarian Cancer Organoids via Immunofluorescence Assays
04:07

Visualizing DNA Damage Repair Proteins in Patient-Derived Ovarian Cancer Organoids via Immunofluorescence Assays

Published on: February 24, 2023

Progression-Free Survival with PARP Inhibitors According to Clinical Risk in Patients with Ovarian Cancer: An

Lorenzo Gasperoni1, Luna Del Bono2, Alberto Farolfi3

  • 1Pharmaceutical Department, USL Toscana Centro, Prato, Italy.

Oncology Research
|June 26, 2026
PubMed
Summary

Poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi) show varying efficacy in ovarian cancer maintenance therapy. Treatment benefit depends on genetic profile and relapse risk, guiding personalized selection.

Keywords:
IPDfromKMadvanced ovarian cancerindirect comparisonpoly (ADP-ribose) polymerase (PARP) inhibitors

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Ovarian Cancer Patient-Derived Organoid Models for Pre-Clinical Drug Testing

Published on: September 15, 2023

Area of Science:

  • Oncology
  • Pharmacology
  • Genetics

Background:

  • Poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi) are standard maintenance therapy for ovarian cancer.
  • Comparative efficacy of PARPi across different genetic profiles and relapse risk categories is not well-defined.

Purpose of the Study:

  • To compare the efficacy of various PARPi as maintenance therapy in ovarian cancer.
  • To analyze treatment outcomes based on genetic profiles (BRCA status, HRD) and relapse risk (high/low).

Main Methods:

  • Reconstructed individual patient data (IPD) from randomized trials (RCTs) using the IPDfromKM method.
  • Progression-free survival (PFS) as the primary endpoint; Restricted Mean Survival Time (RMST) as a supplementary measure.
  • Analysis stratified by BRCA+ high-risk, HRD+/BRCAwt high-risk, and BRCA+ low-risk populations.

Main Results:

  • In BRCA+ high-risk patients, olaparib (alone or with bevacizumab) showed the greatest PFS benefit.
  • Olaparib plus bevacizumab and niraparib had comparable efficacy in HRD+/BRCAwt high-risk patients.
  • Olaparib plus bevacizumab suggested a trend towards superior PFS in BRCA+ low-risk patients compared to olaparib monotherapy.

Conclusions:

  • PARPi efficacy in ovarian cancer is significantly influenced by patient genetic profile and relapse risk.
  • Biomarker-driven selection of PARPi is supported for personalized ovarian cancer treatment strategies.