Abstract
Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.
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Acknowledgements
G.M.B. acknowledges support from the Adelson Medical Research Foundation (AMRF), the Patricia K. Donahoe Award from the Huiying Foundation, the Emma and Bill Roberts MGH Research Scholar Award, the Melanoma Research Alliance and the Department of Defense. A.D. has received support from the George and Marie Vergottis Research Fellowship award from Harvard Medical School. A.M.H. has support provided by the American College of Surgeons Clowes Award and the Department of Defense CDMRP through the Melanoma Academy Scholar Program. S.C. is supported by the KL2 award from Harvard Catalyst/The Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences, NIH Award KL2 TR002542).
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G.M.B. discloses the following financial and professional collaborations: Takeda Oncology — sponsored research agreement; Olink Proteomics — sponsored research agreement; Novartis — scientific advisory board, speaker; Nektar Therapeutics — scientific advisory board, steering committee; Palleon Pharmaceuticals — sponsored research agreement; InterVenn Biosciences — sponsored research agreement, scientific advisory board; Merck — scientific advisory board, consulting; Iovance — scientific advisory board; Ankyra Therapeutics — scientific advisory board, equity. A.P. has held equity in C2i Genomics, XGenomes, Cadex, Vionix and Parithera. In the past 36 months, she has served as an adviser/consultant for Eli Lilly, Mirati, Pfizer, Inivata, Biofidelity, Checkmate Pharmaceuticals, Foundation Medicine Inc., Guardant, AbbVie, Bayer, Delcath, Taiho, CVS, Value Analytics Lab, Seagen, Saga, AstraZeneca, Scare Inc., Illumina, Taiho, Hookipa, Kahar Medical, Xilio Therapeutics, Sirtex, Takeda and Science For America. She receives fees from Up to Date. She has received travel fees from Karkinos Healthcare. She has been on the DSMC for a Roche study and on the Steering Committee for Exilixis. She has received research funding to the Institution from PureTech, PMV Pharmaceuticals, Plexxicon, Takeda, Bristol Myers Squibb, Mirati, Novartis, Erasca, Genentech, Daiichi Sankyo, Syndax, Revolution Medicine and Parthenon. D.L. has received honorarium (invited speaker/travel fees) from Genentech, Inc., and is on the scientific advisory board for Oncovalent Therapeutics. A.M.H., A.D., K.S.-D. and S.C. declare no competing interests.
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Cancer Immune Monitoring and Analysis Centers (CIMACs) and Cancer Immunologic Data Center (CIDC) Network: https://dctd.cancer.gov/ResearchNetworks/cimacs_cidc_network.htm
Personalised Immunotherapy Platform (PIP): https://melanoma.org.au/personalised-immunotherapy-platform-pip/
Glossary
- Area under the curve
-
(AUC). Occurs between two points of a function, which is calculated with the use of a definite integral between the two points. In the context of novel prediction models, a receiver operator curve (ROC) is formed to correlate the true and false positive rates of the test. The AUC of this ROC ranges between 0.5 and 1 and signifies the predictive ability of the model. An AUC of 0.5 means that the model has no predictive value, and an AUC of 1 signifies a perfect predictive model.
- Companion diagnostic test
-
A test performed on a patient before administration of a specific therapy, to determine whether the treatment would have a high chance of working and, therefore, if it can be used for the patient.
- Data coordinating centre
-
(DCC). A group that coordinates the planning and operational aspects of a multi-institutional clinical trial.
- Dynamic biomarkers
-
Measurable biological signals that can change over time and their change can accurately predict, at an early time point, any significant events, such as response to treatment or tumour recurrence after complete response.
- Extracellular vesicles
-
Nano-sized vesicles released from cells. They are enclosed by a single outer membrane, come in various sizes, have variable surface markers and cargo, and can be found in biofluids such as plasma, urine, serum, saliva, cerebrospinal fluid, breast milk, amniotic fluid, semen, lymph, bile and gastric acid.
- Microsatellite instability
-
The existence of a large number of newly identified microsatellite regions in the genome of a sample (in this context cancer cells), which are not found in the corresponding germline DNA. High microsatellite instability is caused by defective DNA mismatch repair of a cell.
- Microsatellite regions
-
Areas in the genome that have a large repetition of microsatellites, which are small repeating nucleotide sequences (of around 2–10 bp) found throughout the genome of each cell.
- Mismatch repair
-
(MMR). A DNA repair system that ensures the integrity of the DNA of each cell. It recognizes and repairs erroneous insertions, deletions and mismatched base pairs that may happen during DNA replication and recombination or at any point during DNA damage.
- Neoantigens
-
Newly formed antigens generated by tumour cells owing to tumour cell-specific alterations, such as dysregulated DNA mutations, RNA splicing and post-translational modifications.
- Next-generation sequencing
-
(NGS). A sequencing technology that enables the processing of multiple DNA sequences in parallel, offering high throughput and speed. NGS is used to determine the nucleotide sequence of either the whole genome or specific DNA and RNA regions of the analysed sample.
- Plasma proteomics
-
The proteomic analysis (that is, the identification of all proteins) of plasma.
- Predictive biomarkers
-
Measurable biological signals that can identify patients who are more likely to respond to a specific therapy.
- Response evaluation criteria in solid tumours
-
(RECIST). A set of published rules used to assess tumour burden with which to provide an objective assessment of response to therapy. They were initially introduced in 2000, and an updated revision (version 1.1) was published in 2009.
- Sensitivity
-
A measure of the proportion of patients who respond to a therapy who were predicted to respond according to a biomarker. It is calculated by dividing the number of biomarker-positive patients who actually respond (true positives) by the total number of patients who actually respond (true positives and false negatives).
- Variant callers
-
After next-generation sequencing, the identified sequences are matched to already available data, to identify any alterations. This process is called ‘variant calling’, and the particular variants identified in a specific sample or sample population are called ‘variant callers’.
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Holder, A.M., Dedeilia, A., Sierra-Davidson, K. et al. Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours. Nat Rev Cancer 24, 498–512 (2024). https://doi.org/10.1038/s41568-024-00705-7
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DOI: https://doi.org/10.1038/s41568-024-00705-7