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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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Related Experiment Video

Updated: Nov 19, 2025

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Published on: December 1, 2023

Data-Driven Methods for Advancing Precision Oncology.

Prema Nedungadi^1,2, Akshay Iyer¹, Georg Gutjahr¹

¹Center for Research in Analytics & Technology in Education, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, India.

Current Pharmacology Reports

|February 1, 2021

Summary

This summary is machine-generated.

Data-driven methods are advancing precision oncology by integrating diverse patient data for tailored cancer treatments. Machine learning and predictive models enhance biomarker discovery and treatment selection, improving patient care.

Keywords:

Artificial intelligence Big data in health Clinical decision support Health analytics Omics Personalized medicine Precision medicine Precision oncology Predictive analytics

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Area of Science:

Biomedical Research
Oncology
Data Science

Background:

Precision oncology tailors cancer treatment using genetic, clinical, environmental, and lifestyle data.
Challenges include managing voluminous, heterogeneous data from diverse sources like Omics and electronic health records.
Statistical and machine learning methods are crucial for handling complex, large-scale biomedical data.

Purpose of the Study:

To review advances, methods, challenges, and future directions of data-driven approaches in precision oncology.
To highlight the role of data analytics in biomedical research, drug discovery, and clinical practice.
To discuss the integration of diverse data modalities for personalized cancer care.

Main Methods:

Review of data-driven methods, including statistical and machine learning algorithms.
Analysis of challenges in handling heterogeneous data from Omics, EHRs, imaging, and wearables.
Exploration of predictive modeling for cancer progression, drug response, and treatment optimization.

Main Results:

Data-driven analytics have accelerated biomarker discovery and drug development in precision oncology.
Predictive models aid in identifying patient populations, forecasting cancer progression, and optimizing combination therapies.
Machine learning and open Omics datasets are key drivers of precision oncology advancements.

Conclusions:

Integrated clinical decision support systems combining EHR and Omics data are essential for data-driven recommendations.
These systems will assist clinicians in disease prevention, early detection, and personalized cancer treatment.
Continuous updates to clinical decision systems are necessary to adapt to evolving cancer knowledge and datasets.