The Combiome Hypothesis: Selecting Optimal Treatment for Cancer Patients

Fred R. Hirsch, Jill Walker, Brandon W. Higgs, Zachary A. Cooper, Rajiv G. Raja, Ignacio I. Wistuba

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations

Abstract

Existing approaches for cancer diagnosis are inefficient in the use of diagnostic tissue, and decision-making is often sequential, typically resulting in delayed treatment initiation. Future diagnostic testing needs to be faster and optimize increasingly complex treatment decisions. We envision a future where comprehensive testing is routine. Our approach, termed the “combiome,” combines holistic information from the tumor, and the patient's immune system. The combiome model proposed here advocates synchronized up-front testing with a panel of sensitive assays, revealing a more complete understanding of the patient phenotype and improved targeting and sequencing of treatments. Development and eventual adoption of the combiome model for diagnostic testing may provide better outcomes for all cancer patients, but will require significant changes in workflows, technology, regulations, and administration. In this review, we discuss the current and future testing landscape, targeting of personalized treatments, and technological and regulatory advances necessary to achieve the combiome.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalClinical Lung Cancer
Volume23
Issue number1
DOIs
StatePublished - Jan 2022

Keywords

  • Antigenicity
  • Co-stimulation
  • Immune activation
  • Immune checkpoint
  • Targeted therapies

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