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PET scan could predict success of checkpoint therapy

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27 November 2018

Checkpoint inhibitor therapy is an exciting new way to treat cancer. It works by inhibiting the mechanism that prevents the patient’s immune system from attacking the tumor. The results have been spectacular, and the discoverers of checkpoints and their inhibitors were awarded the 2018 Nobel Prize in Physiology or Medicine. Unfortunately, not all cancer patients respond to this treatment, and currently available methods are insufficiently predicting their response. But now a team of scientists led by researchers from the University of Groningen Medical Centre (UMCG) in the Netherlands has shown that tumour uptake of a radiolabelled checkpoint inhibitor correlates with future treatment response. Together with colleagues from the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University and the biotech company Genentech (USA), they published their study in Nature Medicine on 26-11-2018.

For many years oncologists have tried to stimulate cancer patients’ own immune system to attack tumour cells, but the results were largely disappointing until the discovery of immune checkpoints. These regulate immune activation, and in tumors cancer patients they prevent an anti-tumor immune response. The discovery of the checkpoints and their mechanism of action led to the development of checkpoint inhibitors:  antibodies that remove this brake on the immune system.

‘The results of checkpoint inhibitors can be really spectacular’, says Prof.  Liesbeth de Vries, medical oncologist at the Department of Medical Oncology at the UMCG. ‘But not all patients respond to the treatment, and we currently do not have reliable indicators to predict who will benefit.’ Immunohistochemical analyses of tumour biopsies assessing the presence of the checkpoint have been inconclusive, with false positives and false negatives.

De Vries: ‘There is growing realization that tumours can be very heterogeneous and dynamic, but a biopsy, besides being invasive, provides only an instantaneous snapshot of a single location. By using PET scans and a radiolabelled antibody, we were able to assess the molecular target throughout the entire body and during a period of one week.’ The scientists published the results from 22 patients with three types of cancer (bladder cancer, non-small cell lung cancer or triple-negative breast cancer) that were presumed to be responsive to checkpoint inhibitors.

The patients were given a tracer dose of atezolizumab, an antibody against the checkpoint PD-L1, labelled with 89Zr, a synthetic isotope of zirconium (Zr). The binding of the antibody to the molecular target was detected by PET scans up to one week after injection. As part of the study subsequently, biopsies were taken and patients received treatment with unlabelled atezolizumab, and the tumour response was studied.

‘We found that the overall response correlated best with the uptake of labelled atezolizumab by the tumour as visualized by the PET scan’, says De Vries. ‘Furthermore, when we looked at individual lesions, the shrinkage of a tumour during the follow up also correlated with the initial uptake of labelled atezolizumab.’ These correlations were much stronger than those obtained by immunohistochemical analysis of the tumour biopsies. Uptake of the checkpoint inhibitor was also seen in the spleen, tumour-free lymph nodes, tonsils and at the sites of resolved infections.

De Vries emphasizes that this was a proof-of-principle study. ‘In a small population we demonstrated that this method can be used to predict patient response to checkpoint inhibitor therapy.’ This initial finding now needs replication in larger patient groups and will be combined with other predictors. ‘Checkpoint inhibitor therapy can have serious side effects for some patients and it is very expensive. So being able to predict who will benefit from checkpoint inhibitors will be a major step forward,’ De Vries concludes.

Reference: Frederike Bensch et al: Clinical 89Zr-atezolizumab imaging as a potential PD-L1 biomarker in cancer patients. Nature Medicine 26-11-2018, DOI: 10.1038/s41591-018-0255-8

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