Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Home
Pan-cancer Drug Response Prediction Using Integrative Principal Component Regression.

Home
Pan-cancer Drug Response Prediction Using Integrative Principal Component Regression.

Related Concept Videos

Cancer Survival Analysis

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Hallmark-guided subtypes of hepatocellular carcinoma for the identification of immune-related gene classifiers in the prediction of prognosis, treatment efficacy, and drug candidates.

Frontiers in immunology·2022

Same author

Preparing Sr-containing nano-structures on micro-structured titanium alloy surface fabricated by additively manufacturing to enhance the anti-inflammation and osteogenesis.

Colloids and surfaces. B, Biointerfaces·2022

Same author

Intraoperative Optical Coherence Tomography in Idiopathic Macular Epiretinal Membrane Surgery.

International journal of general medicine·2022

Same author

Specific gut microbiota alterations in essential tremor and its difference from Parkinson's disease.

NPJ Parkinson's disease·2022

Same author

Microplastics in personal care products: Exploring public intention of usage by extending the theory of planned behaviour.

The Science of the total environment·2022

Same author

Inhibition of Dyrk1A Attenuates LPS-Induced Neuroinflammation via the TLR4/NF-κB P65 Signaling Pathway.

Inflammation·2022

Same journal

Design of Trials with Composite Endpoints with the R Package CompAREdesign.

Statistics in biosciences·2026

Same journal

Variance Estimation for Weighted Average Treatment Effects.

Statistics in biosciences·2026

Same journal

Bayesian Modeling on Microbiome Data Analysis: Application to Subgingival Microbiome Study.

Statistics in biosciences·2026

Same journal

Canopy2: Tumor Phylogeny Inference by Bulk DNA and Single-Cell RNA Sequencing.

Statistics in biosciences·2026

Same journal

Multilevel Multivariate Functional Principal Component Analysis of Evoked and Induced Event-Related Spectral Perturbations.

Statistics in biosciences·2026

Same journal

Robust Privacy-Preserving Models for Cluster-Level Confounding: Recognizing Disparities in Access to Transplantation.

Statistics in biosciences·2026

See all related articles

Related Experiment Video

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression.

Qingzhi Liu¹, Gen Li¹, Veerabhadran Baladandayuthapani¹

¹Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.

Statistics in Biosciences

|June 3, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Precision oncology uses cell lines to study cancer, but they don't fully represent patient tumors. Our new Integrative Principal Component Regression (iPCR) model predicts patient drug responses using genomic data from cell lines and tumors.

Keywords:

Drug response prediction Genomic data integration Network decomposition Precision oncology Principal component regression

Related Experiment Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

Genomics
Computational Biology
Precision Oncology

Background:

Cell lines are crucial for cancer research but have limitations in representing patient tumors.
Bridging the gap between cell line and patient tumor data is essential for precision oncology.
Existing methods lack systematic ways to assess commonalities between cell line and patient tumor genomic data.

Purpose of the Study:

To develop an integrative method for assessing shared variations between cell line and patient tumor genomic data.
To predict patient drug responses using preclinical pharmacological data and integrated genomic variations.
To identify key genes and pathways driving cancer drug responses in patients.

Main Methods:

Introduced the Integrative Principal Component Regression (iPCR) model.
Utilized matrix decompositions to uncover joint and model-specific variations in genomic data.
Applied the model to predict patient drug responses based on cell line pharmacological data.

Main Results:

iPCR effectively uncovers shared and model-specific genomic variations.
The model accurately predicts patient drug responses, outperforming competing methods.
Identified key driver genes and pathways associated with treatment-specific responses across multiple cancers.
Facilitated inference of co-expression networks between cell lines and patients.

Conclusions:

The iPCR model provides a powerful tool for integrating cell line and patient tumor genomic data.
This approach enhances the prediction of patient drug responses in precision oncology.
iPCR aids in identifying novel therapeutic targets and understanding cancer biology.