Cancer Immunotherapy: A promising platform for predicting toxicity
Organ-on-chip approaches could help researchers to better predict the toxicity of cancer immunotherapy drugs.
- Lausanne University Hospital, Switzerland
Killing cancer cells can be done directly or by harnessing the immune system through an approach called cancer immunotherapy (Robert, 2020). Indeed, over the past decade, most of the advances in oncology have involved boosting immune cells to destroy tumor cells. Some of these treatments have resulted in impressive improvements in survival, bringing the possibility of a cure closer for some patients (Robert et al., 2018). This is a big milestone in oncology.
Despite these advances, immunotherapies can be associated with toxicity, which forces the treatment to be stopped: if immune cells become too activated, they can mistakenly recognize and destroy healthy tissues. This can lead, for example, to rashes, hepatitis or colitis depending on whether the skin, liver or gut are attacked (Marin-Acevedo et al., 2019).
A big challenge in oncology is therefore to predict which new immunotherapy drugs are going to be too harmful for patients. This is usually examined in animal models, but since their immune systems differ from the human immune system, it can be difficult to reliably predict toxicity (Zschaler et al., 2014). Now, in eLife, Nikolce Gjorevski (Roche), Lauriane Cabon (Roche) and colleagues – including Jordan Kerns and Chaitra Belgur of Emulate Inc in Boston as joint first authors – report how an in vitro model can help bypass this problem for T cell bispecific antibodies immunotherapy (Kerns et al., 2021).
T cell bispecific antibodies (or TCBs) can recognise and bind to ‘antigen’ proteins present on the surface of tumors, as well as receptors displayed by immune ‘T cells’: by bringing the two types of cells closer, this process helps to activate T cells and allows them to kill their targets. However, the antigens that TCBs bind to are not always exclusive to cancer cells. Recognition of non-cancer cells which share antigens with tumors – known as the on-target, off-tumor effect – can lead to normal cells being damaged (Labrijn et al., 2019; Figure 1). Predicting which TCBs under clinical development will cause such undesired toxicity represents an important challenge in oncology.
Figure 1
Testing drug toxicity using an organ-on-chip platform.
Administering TCBs to an organ-on-chip platform (such as a model of the lung or intestine) allows the antibodies (Y-shaped structures) to recognize antigens (shown in yellow and blue) present in both healthy epithelial and cancerous cells. This brings T cells close to their targets and also activates them, which can lead to healthy cells being damaged (as assessed by measuring cell death and inflammation levels). This model is able to partially reproduce toxicity observed in animals and humans.
Image credit: Image was prepared with Servier Medical Art (CC BY 3.0).
To address this issue, Kerns et al. first took advantage of a lung-on-chip model (Huh et al., 2010) – a system grown under conditions mimicking those found in the body – to predict toxicity to TCBs. This ‘mini-organ’ was exposed to a TCB that recognises an antigen present in ovarian, lung and breast cancer cells, but which can also be expressed, at lower levels, in healthy lung cells. This manipulation led to healthy cells being damaged in the lung-on-chip model, which was then used to determine which TCB dose could kill tumors while sparing normal lung cells. This dose was then administered to mouse models, whose lungs remained undamaged. This demonstrates that the lung-on-chip was able to efficiently predict toxicity in these animals.
Kerns et al. then used an intestine-on-chip model to test a TCB which targets an antigen present on both colon cancer and normal intestine cells. Healthy cells survived the treatment but signs of toxicity emerged that could mimick side-effects that commonly occur in patients, such as increased inflammation.
Using an organ-on-chip model to test TCBs therefore has two main advantages. First, it reproduces toxicity observed in vivo, and can help to determine which doses are effective while remaining safe for normal cells. Second, it serves to predict toxicity before patients are administered with newly-developed TCBs, filling the gap between animal and human studies for drugs that are yet to be clinically tested. Organ-on-chip models could therefore make drug development more efficient by helping to screen out toxic TCBs before they reach cancer patients.
References
-
Bispecific antibodies: a mechanistic review of the pipelineNature Reviews Drug Discovery 18:585–608.https://doi.org/10.1038/s41573-019-0028-1
-
Immune checkpoint inhibitor toxicitiesMayo Clinic Proceedings 94:1321–1329.https://doi.org/10.1016/j.mayocp.2019.03.012
-
Durable complete response after discontinuation of pembrolizumab in patients with metastatic melanomaJournal of Clinical Oncology 36:1668–1674.https://doi.org/10.1200/JCO.2017.75.6270
-
A decade of immune-checkpoint inhibitors in cancer therapyNature Communications 11:3801.https://doi.org/10.1038/s41467-020-17670-y
-
Differences in innate immune response between man and mouseCritical Reviews in Immunology 34:433–454.https://doi.org/10.1615/CritRevImmunol.2014011600
Article and author information
Author details
Maria Ochoa de Olza
Publication history
- Version of Record published: September 24, 2021 (version 1)
Copyright
© 2021, Ochoa de Olza
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 784
- Page views
-
- 62
- Downloads
-
- 1
- Citations
Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Cancer Immunotherapy: A promising platform for predicting toxicity
eLife 10:e73191.
https://doi.org/10.7554/eLife.73191
Further reading
-
- Cancer Biology
- Computational and Systems Biology
Colorectal cancer (CRC) remains a challenging and deadly disease with high tumor microenvironment (TME) heterogeneity. Using an integrative multi-omics analysis and artificial intelligence-enabled spatial analysis of whole-slide images, we performed a comprehensive characterization of TME in colorectal cancer (CCCRC). CRC samples were classified into four CCCRC subtypes with distinct TME features, namely, C1 as the proliferative subtype with low immunogenicity; C2 as the immunosuppressed subtype with the terminally exhausted immune characteristics; C3 as the immune-excluded subtype with the distinct upregulation of stromal components and a lack of T cell infiltration in the tumor core; and C4 as the immunomodulatory subtype with the remarkable upregulation of anti-tumor immune components. The four CCCRC subtypes had distinct histopathologic and molecular characteristics, therapeutic efficacy, and prognosis. We found that the C1 subtype may be suitable for chemotherapy and cetuximab, the C2 subtype may benefit from a combination of chemotherapy and bevacizumab, the C3 subtype has increased sensitivity to the WNT pathway inhibitor WIKI4, and the C4 subtype is a potential candidate for immune checkpoint blockade treatment. Importantly, we established a simple gene classifier for accurate identification of each CCCRC subtype. Collectively our integrative analysis ultimately established a holistic framework to thoroughly dissect the TME of CRC, and the CCCRC classification system with high biological interpretability may contribute to biomarker discovery and future clinical trial design.
-
- Cancer Biology
In embryonal rhabdomyosarcoma (ERMS) and generally in sarcomas, the role of wild-type and loss or gain-of-function TP53 mutations remains largely undefined. Eliminating mutant or restoring wild-type p53 is challenging; nevertheless, understanding p53 variant effects on tumorigenesis remains central to realizing better treatment outcomes. In ERMS, >70% of patients retain wild-type TP53, yet mutations when present are associated with worse prognosis. Employing a kRASG12D-driven ERMS tumor model and tp53 null (tp53-/-) zebrafish, we define wild-type and patient-specific TP53 mutant effects on tumorigenesis. We demonstrate that tp53 is a major suppressor of tumorigenesis, where tp53 loss expands tumor initiation from <35% to >97% of animals. Characterizing three patient-specific alleles reveals that TP53C176F partially retains wild-type p53 apoptotic activity that can be exploited, whereas TP53P153D and TP53Y220C encode two structurally related proteins with gain-of-function effects that predispose to head musculature ERMS. TP53P153D unexpectedly also predisposes to hedgehog expressing medulloblastomas in the kRASG12D-driven ERMS-model.
