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Personalized Cancer Treatment and Patient Stratification Using Massive Parallel Sequencing (MPS) and Other OMICs Data

  • Mark Abramovitz
  • Casey Williams
  • Pradip K. De
  • Nandini Dey
  • Scooter Willis
  • Brandon Young
  • Eleni Andreopoulou
  • W. Fraser Symmans
  • Jason K. Sicklick
  • Razelle Kurzrock
  • Brian Leyland-Jones
Chapter

Abstract

Cancer research is moving at a startling pace, most particularly with the identification and characterization of molecular signatures and biomarkers that will be used for prevention, early detection, diagnosis, treatment, prediction, and prognostication. The goal of cancer therapy is to match the right treatment with the right patient. To this end, several clinical trials are now employing patient stratification using “Massive Parallel Sequencing” or informally called “Next -Generation Sequencing” (NGS) to identify clinically actionable targets in real time.

The omics revolution is yielding important new insights into the causes and mechanisms of diseases and drug responses and in understanding the effects of genes and environment in disease predisposition and acquired resistance. It is paving the way for precision medicine (PM) a.k.a. personalized medicine, which focuses its attention on factors specific to an individual patient to provide individualized care; information about a patient’s genes, proteins, and environment is used to prevent, diagnose, and tailor medical care to that of the individual. The use of NGS and omics data is currently revolutionizing how cancer patients are treated. Targeted therapies that take advantage of the knowledge about an individual’s specific cancer cells are currently being applied to treat many different types of cancer.

PM also requires that we address the multidimensionality of cancer biomarkers. PM will require a systems biology approach integrating omics platform data to develop novel probabilistic models that can be applied to early detection, prognosis, prediction, and prevention. Ultimately, PM will be based on real-time profiling of an individual’s tumor, which translates into optimized treatment that will extend both overall survival and quality of life.

Keywords

Massive parallel sequencing Next-generation sequencing Omics Signaling pathways Personalized medicine 

Notes

Acknowledgments

We would like to acknowledge the Avera Cancer Institute, Sioux Falls, SD, for providing support for our research.

Conflicts of Interest

The authors have no conflicts of interest to declare.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mark Abramovitz
    • 1
  • Casey Williams
    • 2
  • Pradip K. De
    • 3
  • Nandini Dey
    • 4
  • Scooter Willis
    • 2
  • Brandon Young
    • 2
  • Eleni Andreopoulou
    • 5
  • W. Fraser Symmans
    • 6
  • Jason K. Sicklick
    • 7
  • Razelle Kurzrock
    • 8
  • Brian Leyland-Jones
    • 4
  1. 1.Avera Cancer InstituteSioux FallsUSA
  2. 2.Center for Precision OncologyAvera Cancer InstituteSioux FallsUSA
  3. 3.Department of Molecular and Experimental MedicineAvera Cancer InstituteSioux FallsUSA
  4. 4.Department of Molecular and Experimental Medicine, Center for Precision OncologyAvera Cancer InstituteSioux FallsUSA
  5. 5.Department of Medicine, Division of Hematology & Medical OncologyWeill Cornell Medicine/New York Presbyterian HospitalNew YorkUSA
  6. 6.MD Anderson Cancer CenterHoustonUSA
  7. 7.Division of Surgical Oncology, General Surgery Residency, Biorepository and Tissue Technology Shared Resource, Moores Cancer CenterUniversity of California San Diego (UCSD), School of MedicineSan DiegoUSA
  8. 8.Moores Cancer Center, UC San Diego Moores Cancer CenterLa JollaUSA

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