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Exploiting temporal aspects of cancer immunotherapy

Nature Reviews Cancer volume 24, pages 480–497 (2024)Cite this article

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Abstract

Many mechanisms underlying an effective immunotherapy-induced antitumour response are transient and critically time dependent. This is equally true for several immunological events in the tumour microenvironment induced by other cancer treatments. Immune checkpoint therapy (ICT) has proven to be very effective in the treatment of some cancers, but unfortunately, with many cancer types, most patients do not experience a benefit. To improve outcomes, a multitude of clinical trials are testing combinations of ICT with various other treatment modalities. Ideally, those combination treatments should take time-dependent immunological events into account. Recent studies have started to map the dynamic cellular and molecular changes that occur during treatment with ICT, in the tumour and systemically. Here, we overlay the dynamic ICT response with the therapeutic response following surgery, radiotherapy, chemotherapy and targeted therapies. We propose that by combining treatments in a time-conscious manner, we may optimally exploit the interactions between the individual therapies.

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Fig. 1: The dynamic events underlying immune checkpoint therapy efficacy.
Fig. 2: The cellular and molecular dynamics of the tumour microenvironment following standard of care treatment.
Fig. 3: Clinical application of time-dependent precision immuno-oncology: what it could look like.

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Acknowledgements

G.V.L. is supported by a National Health and Medical Research Council (NHMRC) Investigator Grant (2021/GNT2007839) and by the University of Sydney Medical Foundation. W.J.L. is supported by an NHMRC Investigator Grant (2021/GNT1196605).

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Authors and Affiliations

  1. Telethon Kids Institute, University of Western Australia, Perth, Western Australia, Australia

    Rachael M. Zemek & Willem Joost Lesterhuis

  2. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Rachael M. Zemek

  3. The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Valsamo Anagnostou

  4. Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia

    Inês Pires da Silva & Georgina V. Long

  5. Faculty of Medicine & Health, The University of Sydney, Sydney, New South Wales, Australia

    Inês Pires da Silva & Georgina V. Long

  6. Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia

    Inês Pires da Silva & Georgina V. Long

  7. Crown Princess Mary Cancer Centre Westmead, Blacktown Hospital, Sydney, New South Wales, Australia

    Inês Pires da Silva

  8. Royal North Shore and Mater Hospitals, Sydney, New South Wales, Australia

    Georgina V. Long

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All authors researched data for the article, contributed substantially to the discussion of the content, wrote the article and reviewed the manuscript before submission.

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Correspondence to Willem Joost Lesterhuis.

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Competing Interests

R.M.Z. is an inventor on patent applications (WO/2019/178650A1 and PCT/AU2023/051218) related to ICT treatment combinations. V.A. receives research funding to Johns Hopkins University from AstraZeneca and Personal Genome Diagnostics, has received research funding to Johns Hopkins University from Bristol-Myers Squibb and Delfi Diagnostics in the past 5 years, is an advisory board member for AstraZeneca and Neogenomics and receives honoraria from Foundation Medicine and Personal Genome Diagnostics. V.A. is an inventor on patent applications (63/276,525, 17/779,936, 16/312,152, 16/341,862, 17/047,006 and 17/598,690) submitted by Johns Hopkins University related to cancer genomic analyses, ctDNA therapeutic response monitoring and immunogenomic features of response to immunotherapy that have been licensed to one or more entities. Under the terms of these license agreements, the university and inventors are entitled to fees and royalty distributions. I.P.S. had travel support by BMS and MSD, speaker fee by Pierre Fabre, Roche, BMS and MSD and has served as consultant on advisory boards from MSD. G.V.L. is consultant adviser for Agenus, Amgen, Array Biopharma, AstraZeneca, Bayer, BioNTech, Boehringer Ingelheim, Bristol Myers Squibb, Evaxion, Hexal AG (Sandoz Company), Highlight Therapeutics S.L., IOBiotech, Immunocore, Innovent Biologics USA, Merck Sharpe & Dohme, Novartis, PHMR Ltd, Pierre Fabre, Regeneron, Scancell and SkylineDX B.V. W.J.L. is consultant adviser for Douglas Pharmaceuticals, has received research funding from Douglas Pharmaceuticals, AstraZeneca and Axelia Oncology. W.J.L. is a founder and director of Setonix Pharmaceuticals and is inventor on patent applications (WO/2016/015095, WO/2019/178650A1 and PCT/AU2023/051218) related to ICT treatment combinations.

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Supplementary information

Glossary

Adjuvant treatment

A treatment given after surgery to reduce the chance of cancer recurrence.

Circulating tumour DNA

(ctDNA). Circulating tumour-derived fragmented DNA that can be used to assess minimal residual disease or capture intratumoural and temporal cancer heterogeneity.

Clonotype richness

A measure of the number of T cell clones with a unique T cell receptor, wherein a higher clonotype richness indicates a wider array of T cell receptors within the T cell population.

Co-stimulatory molecules

A cell surface protein that provides activation signals alongside primary antigen recognition to fully activate T cells.

Effector memory T cell

A subset of memory T cells with effector activity.

Fractionated doses

A delivery method of radiation to a site in which the total prescribed dose is divided into smaller doses over a period of time.

Immune dynamics

Series of immune-mediated biological events that occur in a sequence over time.

Myeloid-derived suppressor cell

(MDSC). A myeloid cell that arises under pathological conditions, defined by its T cell immunosuppressive functions.

Neoadjuvant treatment

A treatment given (partially or completely) before surgery to increase the likelihood of complete surgical resection and to reduce the chance of cancer recurrence.

Progenitor exhausted CD8+ T cells

(Tpex). Self-renewing CD8+ T cells with memory characteristics and high PD1 expression that are defined by the expression of the transcription factors TCF1 (encoded by the gene TCF7) and MYB, which replenish terminally exhausted effector cells.

Resident memory T cell

A subset of memory T cells that persist in tissue in which they were locally activated.

Terminally exhausted T cells

(Tex). Tpex cells give rise to Tex cells marked by CD38, CD39, PD1 and TIM3, which have diminished responsiveness to ICT, yet are the dominant phenotype of tumour antigen-specific T cells.

Tertiary lymphoid structures

Formations of ectopic lymphoid organs within tumours that consist of B cells, T cells and supporting dendritic cells.

Tumour-associated macrophages

A monocyte or macrophage-derived cells expressing macrophage markers, which can have a pro-inflammatory phenotype with antitumour functionality or an anti-inflammatory phenotype with tumour promoting effects.

Tumour microenvironment

(TME). The immediate environment of a tumour that includes blood vessels, immune cells, signalling molecules, fibroblasts and extracellular matrix.

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Zemek, R.M., Anagnostou, V., Pires da Silva, I. et al. Exploiting temporal aspects of cancer immunotherapy. Nat Rev Cancer 24, 480–497 (2024). https://doi.org/10.1038/s41568-024-00699-2

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