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Immune Checkpoint Blockade Response Biomarkers

  • Living reference work entry
  • First Online:
Handbook of Cancer and Immunology

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Abstract

Cancer immunotherapy based on immune checkpoint blockade (ICB) has demonstrated durable clinical benefit in patients with immunogenic tumors, representing a new alternative in the metastatic scenario. However, innate and acquired resistance is the Achilles heel of this revolutionary therapeutic approach, together with the occurrence of mostly autoimmune adverse effects, particularly in the most effective regimen, the combination immunotherapy. This, together with the high treatment cost, has triggered an important crusade to identify biomarkers that can be used to detect ICB responders before and during treatment that could be used for further clinical stratification and monitoring. Because ICB implementation is relatively recent, the initial works in the field of mechanisms and biomarkers of resistance included very modest cohorts; however, that limitation led to a maximized exploitation of the clinical material, which yielded biomarkers on most OMICS, from genomics to microbiome or radiogenomics. One of the most critical areas of current immunotherapy research is the urgent finding of specific novel predictive biomarkers that could identify individuals who would benefit from ICB. The objective of this chapter is to provide an overview of the current knowledge on checkpoint biology by reviewing the different emerging therapeutically relevant immune checkpoint blockade response biomarkers and their modulation of immune checkpoint signaling at multiple levels.

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Abbreviations

ADCC:

Antibody-dependent cell-mediated cytotoxicity

ALK:

Anaplastic lymphoma kinase

APC:

Antigen presenting cell

BER:

Base excision repair

bTMB:

Blood-based TMB

CNA:

Copy number aberrations

CR:

Complete response

CRP:

C-reactive protein

CTC:

Circulating tumor cell

ctDNA:

Circulating tumor DNA

CTLA-4:

Cytotoxic T lymphocyte associated protein 4

DAI:

Agretopicity index

DAMP:

Damage-associated molecular pattern

DC:

Dendritic cell

DDR:

DNA damage response

dMMR:

MMR deficiency

DR:

Direct repair

EBV:

Epstein-Barr virus

EFGR:

Epidermal growth factor receptor

EMT:

Epithelial-to-mesenchymal transition

EPCAM:

Epithelial Cell Adhesion Molecule

FDA:

US Food and Drug Administration

HCC:

Hepatocellular carcinoma

HPV:

Human papillomavirus

HRR:

Homologous recombination repair

ICB:

Immune Checkpoint Blockade

ICOS:

Inducible T cell co-stimulator

IFN:

Interferon

IHC:

Immunohistochemistry

IL:

Interleukin

indel:

Insertions and deletions

ITH:

Intra-tumor heterogeneity

KN:

KEYNOTE

LAG-3:

Lymphocyte activation gene 3

LDH:

Lactate dehydrogenase

LOF:

Loss of function

LOH:

Loss of heterogeneity

MHC:

Major histocompatibility complex

MMR:

Mismatch repair

MSI:

Microsatellite instability

NER:

Nucleotide excision repair

NHEJ:

Non-homologous end-joining

NLR:

Neutrophil-to-lymphocyte ratio

NSCLC:

Non-small cell lung cancer

ORF:

Open reading frame

ORR:

Overall response ratio

OS:

Overall survival

PD-1:

Programmed cell death 1

PD-L1:

Programmed cell death ligand 1

PFS:

Progression-free survival

POLE/POLD1:

DNA polymerase gene epsilon/delta 1

PR:

Partial response

PRR:

Pattern-recognition receptor

scRNA-seq:

Single-cell mRNA sequencing

SNV:

Single-nucleotide variation

TAM:

Tumor-associated macrophages

TCGA:

The Cancer Genome Atlas

TIL:

Tumor Infiltrating Lymphocytes

TMB:

Tumor mutation burden

TME:

Tumor microenvironment

TNF:

Tumor necrosis factor

tNGS:

Next Generation Sequencing

USD:

United States dollar

WES:

Whole exome sequencing

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Acknowledgments

The work is funded by Instituto de Salud Carlos III through the projects PI18_01592 and PI22_01816 (co-funded by the European Regional Development Fund/European Social Fund “A way to make Europe”/“Investing in your future”); Sociedad Española de Oncología Médica (SEOM); Sistema Andaluz de Salud, through the projects SA 0263/2017, Nicolás Monardes (to IB), PI-0121-2020, and RH-0090-2020; Consejería de Transformación económica, Industria, Conocimiento y Universidades through the projects CV20-62050, PY20_01326 and ProyExcel_01002; Spanish Group of Melanoma (Award for Best Research Project 2020); Fundación Bancaria Unicaja through the project C19048; and Andalusia-Roche Network Mixed Alliance in Precision Medical Oncology.

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  1. María Garrido-Barros and Patricia Chaves contributed equally.

Authors and Affiliations

  1. Medical Oncology Service (Group of Translational Research in Cancer Immunotherapy), Regional and Virgen de la Victoria University Hospitals, Institute of Biomedical Research in Malaga and Nanomedicine Platform (IBIMA-BIONAND platform), Málaga, Spain

    María Garrido-Barros, Patricia Chaves & Isabel Barragán

  2. Group of Pharmacoepigenetics, Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden

    Isabel Barragán

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  1. María Garrido-Barros
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  2. Patricia Chaves
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  3. Isabel Barragán
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  1. Division of Clinical Immunology, Karolinska University Hospital, Huddinge, Sweden

    Nima Rezaei

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Garrido-Barros, M., Chaves, P., Barragán, I. (2023). Immune Checkpoint Blockade Response Biomarkers. In: Rezaei, N. (eds) Handbook of Cancer and Immunology. Springer, Cham. https://doi.org/10.1007/978-3-030-80962-1_160-1

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