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New tool predicts patientsí immunotherapy response
08-27-2020
by Mel J. Yeates  |  Email the author
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BATH, U.K.—A new diagnostic tool to predict whether a cancer patient will respond to immunotherapy treatment has been developed by scientists at the University of Bath. This technology will reportedly allow clinicians to tailor treatments specifically to patients, and avoid treatment paths that are unlikely to be successful.
 
“The tool we have developed is an important step towards personalized medicine. By using it, we can precisely select who will benefit from immunotherapy,” said Professor Stephen Ward, vice-chair of the Centre for Therapeutic Innovation (CTI-Bath) and a co-author of the study. “It will also show which patients are unlikely to respond well before they start a long course of treatment, and these patients can be offered a different treatment route.”
 
While checkpoint inhibitor immunotherapy is very successful in some patients, in others it has little or no effect. Given the fact that there are inherent toxicity risks in these treatments, there is a growing need to determine the patients most likely to benefit — avoiding unnecessary exposure to those who will not.
 
“Immune checkpoint blockade is becoming a therapeutic milestone in some cancers in the last years. Patients are selected for this treatment option using immunohistochemistry; however, this technique does not reliably detect all of the candidates that would potentially benefit,” noted Professor José I López, Department of Pathology, Cruces University Hospital in Bilbao, Spain, and co-author of this study. “Actually, up to 19% of patients supposedly negative do respond to this therapy.”
 
Researchers led by Professor Banafshé Larijani, director of CTI-Bath, worked with other colleagues as well as FASTBASE Solutions Ltd. to develop a prognostic tool using an advanced microscopy platform that identifies immune cell interactions with tumor cells. It also reports on the activation status of immune-checkpoints that dampen the anti-tumor response.
 
The findings were published in Cancer Research, a journal of the American Association for Cancer Research.
 
The article points out that the researchers “have developed and tested an imaging assay that provides a quantitative readout of immune-checkpoint interaction between cells. iFRET (immune-FRET) employs a two-site, cell-cell amplified Förster Resonance Energy Transfer method, detected by Fluorescence Lifetime Imaging Microscopy (FRET/FLIM). Here, iFRET acts as a ‘chemical ruler,’ measuring cell-cell interactions in the range of 1-10nm.”
 
Ordinarily, when PD-1 on the surface of T lymphocytes engages with PD-L1 on the surface of other immune cells, it effectively switches off the immune function of the T cell. In a healthy individual, these checkpoints tightly regulate the body’s immune response — acting as an off-switch to prevent autoimmune and inflammatory diseases.
 
Tumor cells are able to hijack this mechanism by expressing PD-L1 on their surfaces, enabling them to activate PD-1 on the T lymphocyte — thus switching off its anti-tumor function, allowing survival and the growth of the tumor. Immunotherapy checkpoint inhibitors work by disrupting the interaction between the PD-L1 on the tumor and PD-1 on the T cell, and reestablish the patient’s anti-tumor activity.
 
“Currently, decisions on whether to proceed with checkpoint inhibitor treatment are based simply on whether PD-1 and PD-L1 are present in biopsies, rather than their functional state,” explained Larijani. “However, our work has shown it is far more important to know that the two proteins are actually interacting and therefore likely to be having a functional impact on tumor survival.”
 
“In both melanoma and NSCLC, it was shown that PD-L1 expression levels were unable to predict patient outcome,” the article states. “This questions current protocols which rely on IHC PD-L1 expression levels to predict patient outcome and thus has implications for the use of simple expression levels to stratify patients for treatment. Moreover, in ccRCC patients, high interaction states were observed in patients who would otherwise be labelled as PD-L1 negative.”
 
“Blockade of interaction would be predicted to be effective in contexts where elevated levels of interaction occurs and is by inference responsible for the immune privileged state of the tumour. Hence interaction would a priori be a criterion for treatment,” continues the article.
 
The new diagnostic tool determines the extent of PD-1/PD-L1 interaction in a biopsy of the tumor, predicting whether the checkpoint inhibitor therapy is likely to have significant clinical benefit. The results show that immunotherapy-treated patients with metastatic non-small cell lung cancer who displayed a low extent of PD-1/PD-L1 interaction show significantly worse outcome than those with a high interaction.
 
“iFRET can be exploited to monitor other intercellular protein interactions and there are ongoing developments designed to capture related immune modulatory interactions pertinent to cancer and emerging cancer treatments,” the article notes. “This provides the potential for iFRET to become a useful predictive tool informing on the nature of the tumor immune-privileged state.”
 
“Whilst single region analysis has here provided insight into treatment responses, multi-regional analysis may provide a more comprehensive view. Furthermore, as a principle, it is clear that this approach has capabilities beyond immune-tumour cell interactions and the broader uptake of the approach promises to be informative in many research (e.g. axon guidance) and clinical (e.g. angiopathies) settings,” concludes the article.
 
Researchers next plan to implement this imaging platform in national and international trials, in order to assess how this quantitative prognostic tool may be used as a companion diagnostic.
 
“We find this technology and its application in the field of immunotherapy truly interesting. Therefore, we are going to carry out a clinical trial in three hospitals of BioCruces and BioDonostia, the Basque Public Health network, that will allow us to evaluate the predictive capacity of this quantitative imaging platform, to improve patient stratification for lung cancer immunotherapy,” stated Dr. Eunate Arana, scientific coordinator of BioCruces Health Research Institute.
 
Code: E08272001

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