Current Pharmacology Reports

, Volume 4, Issue 2, pp 145–156 | Cite as

Data-Driven Methods for Advancing Precision Oncology

  • Prema Nedungadi
  • Akshay Iyer
  • Georg Gutjahr
  • Jasmine Bhaskar
  • Asha B. Pillai
Precision Medicine and Pharmacogenomics (S Nair, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Precision Medicine and Pharmacogenomics


Purpose of Review

This article discusses the advances, methods, challenges, and future directions of data-driven methods in advancing precision oncology for biomedical research, drug discovery, clinical research, and practice.

Recent Findings

Precision oncology provides individually tailored cancer treatment by considering an individual’s genetic makeup, clinical, environmental, social, and lifestyle information. Challenges include voluminous, heterogeneous, and disparate data generated by different technologies with multiple modalities such as Omics, electronic health records, clinical registries and repositories, medical imaging, demographics, wearables, and sensors. Statistical and machine learning methods have been continuously adapting to the ever-increasing size and complexity of data. Precision Oncology supportive analytics have improved turnaround time in biomarker discovery and time-to-application of new and repurposed drugs. Precision oncology additionally seeks to identify target patient populations based on genomic alterations that are sensitive or resistant to conventional or experimental treatments. Predictive models have been developed for cancer progression and survivorship, drug sensitivity and resistance, and identification of the most suitable combination treatments for individual patient scenarios. In the future, clinical decision support systems need to be revamped to better incorporate knowledge from precision oncology, thus enabling clinical practitioners to provide precision cancer care.


Open Omics datasets, machine learning algorithms, and predictive models have enabled the advancement of precision oncology. Clinical decision support systems with integrated electronic health record and Omics data are needed to provide data-driven recommendations to assist clinicians in disease prevention, early identification, and individualized treatment. Additionally, as cancer is a constantly evolving disorder, clinical decision systems will need to be continually updated based on more recent knowledge and datasets.


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



Our work derives inspiration and guidance from the Chancellor of Amrita Vishwa Vidyapeetham, Sri Mata Amritanandamayi Devi.

Compliance with Ethical Standards

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Human and Animal Rights and Informed Content

This article does not contain any studies with human or animal subjects performed by any of the authors.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Prema Nedungadi
    • 1
    • 2
  • Akshay Iyer
    • 1
  • Georg Gutjahr
    • 1
  • Jasmine Bhaskar
    • 1
    • 2
  • Asha B. Pillai
    • 3
  1. 1.Center for Research in Analytics & Technology in EducationAmrita Vishwa VidyapeethamKollamIndia
  2. 2.Department of Computer Science, School of EngineeringAmrita Vishwa VidyapeethamKollamIndia
  3. 3.Division of Pediatric Hematology/Oncology, Departments of Pediatrics and Microbiology and ImmunologyUniversity of Miami Miller School of MedicineMiamiUSA

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