151
Views
0
CrossRef citations to date
0
Altmetric
Review

Next generation immuno-oncology biomarkers in gastrointestinal cancer: what does the future hold?

ORCID Icon, ORCID Icon, , &
Pages 863-873 | Received 24 Jun 2023, Accepted 24 Aug 2023, Published online: 31 Aug 2023
 

ABSTRACT

Introduction

Gastrointestinal (GI) cancers pose a significant health burden worldwide, necessitating advancements in diagnostic and treatment approaches. One promising avenue is the utilization of next-generation biomarkers, which hold the potential to revolutionize GI cancer management.

Areas Covered

This review explores the latest breakthroughs and expert opinions surrounding the application of next-generation immunotherapy biomarkers. It encompasses various aspects of the currently utilized biomarkers of immunotherapy in the context of GI cancers focusing on microsatellite stable cancers. It explores the promising research on the next generation of biomarkers addressing the challenges associated with integrating them into clinical practice and the need for standardized protocols and regulatory guidelines.

Expert Opinion

Immune profiling, multiplex immunohistochemistry, analysis of immune cell subsets, and novel genomic and epigenomic markers integrated with machine-learning approaches offer new avenues for identifying robust biomarkers. Liquid biopsy-based approaches, such as circulating tumor DNA (ctDNA) and exosome-based analyses, hold promise for real-time monitoring and early detection of treatment response.

Article highlights

  • Despite recent progress in immunotherapy, survival rates for many GI cancers remain suboptimal, necessitating innovative diagnosis and treatment strategies.

  • PD-L1 expression, HER2 overexpression/amplification, and tumor mutational burden (TMB) have been explored as a potential biomarker for ICI response in GI cancers.

  • Clinically established biomarkers face challenges such as detection method heterogeneity, spatial and temporal heterogeneity, reproducibility concerns, and limited predictive value and clinical utility

  • The evolution of next-generation biomarkers of immunotherapy such as DNA repair genes, microbiome, and integration of machine learning-based approaches could advance precision oncology by improving diagnostic, staging, predictive, and prognostic approaches using clinical, omics, and imaging data.

  • Immune profiling, advanced genomic and epigenomic markers, and liquid biopsy-based approaches show promise for improving personalized treatment strategies.

Declaration of interest

Anwaar Saeed reports research grants (to institution) from AstraZeneca, Bristol Myers Squibb, Merck, Clovis, Exelixis, Actuate Therapeutics, Incyte Corporation, Daiichi Sankyo, Five Prime Therapeutics, Amgen, Innovent Biologics, Dragonfly Therapeutics, KAHR medical, Biontech, and advisory board fees from AstraZeneca, Bristol Myers Squibb, Exelixis, Pfizer, and Daiichi Sankyo. I.H.S. received Advisory Board fees from Seattle Genetics, GSK, and Lumanity. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewers disclosure

Peer reviewers on this manuscript have no relevant financial relationships or otherwise to disclose.

Additional information

Funding

This paper was not funded.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 99.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 706.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.