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Related Concept Videos

Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase01:11

Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase

Genetic polymorphisms in drug targets have emerged as critical determinants of interindividual variability in drug response and toxicity. Pharmacogenomic investigations increasingly focus on identifying these variations to personalize and optimize therapeutic interventions. A drug target may be a receptor, enzyme, or signaling protein involved in pharmacologic responses or disease-related pathways. While early pharmacogenetic studies focused primarily on drug metabolism, current research...
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Related Experiment Video

Updated: Jun 3, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Predictive algorithms for adjuvant therapy: TransATAC.

Mitch Dowsett1, Janine Salter, Lila Zabaglo

  • 1Academic Department of Biochemistry, Royal Marsden Hospital, Fulham Road, London SW36JJ, UK. mitch.dowsett@icr.ac.uk

Steroids
|April 8, 2011
PubMed
Summary
This summary is machine-generated.

Predicting breast cancer recurrence is crucial for treatment. The IHC4 score, combining ER, PgR, HER2, and Ki67, effectively predicts outcomes, similar to the 21-gene recurrence score.

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Last Updated: Jun 3, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

  • Oncology
  • Translational Research
  • Biomarker Discovery

Background:

  • Estrogen receptor (ER) positive breast cancers exhibit variable clinical outcomes.
  • Accurate prediction of disease course is essential for guiding treatment decisions.

Purpose of the Study:

  • To evaluate individual and multiparameter biomarkers for predicting overall and distant recurrence in ER-positive primary breast cancer.
  • To compare the predictive performance of novel biomarker combinations against established scores.

Main Methods:

  • Analysis of biomarkers including ER, PgR, HER2, and Ki67 from the TransATAC trial.
  • Development and assessment of the IHC4 score as a multiparameter predictor.
  • Comparison of IHC4 with the 21-gene recurrence score (RS) and clinicopathologic variables.

Main Results:

  • No biomarkers showed differential benefit between anastrozole and tamoxifen treatments.
  • ER, PgR, HER2, and Ki67 were each associated with recurrence risk.
  • The IHC4 score demonstrated predictive accuracy comparable to the 21-gene RS.
  • Combining molecular profiles with clinicopathologic variables yielded the most accurate outcome prediction.

Conclusions:

  • The IHC4 score is a valuable tool for predicting recurrence in ER-positive breast cancer.
  • Integration of molecular biomarkers with clinical data enhances prognostic accuracy.
  • These findings support personalized treatment strategies based on comprehensive risk assessment.