The paper, published in the March issue of Cancer Immunology, Immunotherapy, validates the use of this platform in a pre-clinical setting to demonstrate tumor sensitivity to immune checkpoint inhibitors alone or in combination with other drugs, it said.
The research marks significant progress towards successfully predicting immunotherapy response in the clinic, added the cancer diagnostics firm.
While clinical prediction is still in progress, KIYATEC said that pharmaceutical companies can today utilize its technology to make meaningful decisions during their drug development process.
Effective pairing of immunotherapies and patient
The South Carolina headquartered company said the study highlights how it has the ability to model the immune microenvironment of a patient, and then measure checkpoint inhibitor response alone or in combination with PARP inhibitors. Most checkpoint inhibitors are used in combination with other drugs and the PARP inhibitor combination is particularly relevant in ovarian cancer, it explained.
It outline how immune checkpoint inhibitors that target programmed cell death protein 1 (PD-1) and programmed death-ligand 1 (PD-L1) have only shown modest activity as monotherapies for the treatment of ovarian cancer. Costing a premium, in many cases only 10-30% of patients respond to such treatments, said the diagnostics developer.
So there is a great need and opportunity to better predict which patients will respond to these immunotherapies.
Indeed, the importance of checkpoint inhibitors meeting key clinical endpoints has recently been brought into focus in more than one cancer indication, with four US withdrawals to date in 2021.
Roche announced it was removing the US indication for Tecentriq (atezolizumab) earlier this month in metastatic urothelial carcinoma (mUC) previously treated with chemo, and AstraZeneca withdrew the US indication for Imfinzi in previously treated bladder cancer, likewise in the case of Merck & Co for Keytruda in previously treated metastatic small cell lung cancer, and BMS for Opdivo in small cell lung cancer after chemo.
Those developments underscore the growing need for more effective pairing of immunotherapies and patient, said KIYATEC. It says its platform has the potential to address this critical issue.
In conversation with the CEO...
We got a deep dive into the new study and the merits of the platform with KIYATEC founder and CEO, Matthew Gevaert:
BioPharma-Reporter: How did the company model the immune microenvironment of a patient, and then measure checkpoint inhibitor response alone or in combination with PARP inhibitors?
Matthew Gevaert: We model a patient’s tumor immune microenvironment by creating 3D spheroids from their live tumor tissue. The patient-specific 3D spheroids that we make contain all the different types of cells that were in the patient’s tumor, including tumor cells and immune cells. Using this method, we create a living model of their tumor and the immune system within the tumor. This is what differentiates KIYATEC’s platform from other methods of treatment selection, such as the PD-1 biomarker. We model the actual patient’s living tumor environment which provides biological evidence of response to treatment, versus providing a statistical probability of response from biomarkers.
In this study, we treated the 3D spheroids with checkpoint inhibitors at a range of doses and measured the resulting dead tumor cells to determine the effectiveness of the drugs. In some cases, we added PARP inhibitors in combination with the checkpoint inhibitors. We note that since we need live tumor tissue for our test, this must be coordinated with a planned surgery for the patient. For many cancers, surgery usually occurs as standard of care, so the patient is already undergoing a planned surgery.
BPR: How does the company’s platform tease out intricate biological interactions between tumor cells, immune cells, and therapeutics in ways that are not possible in patients?
MG: There is tremendous value in developing information about a tumor’s response to treatment in a lab, not in the patient’s body. This platform provides the opportunity of exploring what is going to happen to a patient’s tumor before treatment, which reduces unnecessary biological toxicity and financial cost. The hundreds of 3D spheroids that can be made from a patient’s tumor provide multiple opportunities to test different drugs or drug combinations. This cannot be done in a patient – a patient is prescribed one treatment (or combination) at a time and the oncology team must wait weeks or months to see if it works. Additionally, using a patient’s live cells outside their body means that multiple drugs can be tested in parallel, and physicians can select the treatment drug combination based on evidence. Outside the body, drug testing across a range of doses provides more context on the tumor’s response to the drug, which improves decision-making.
BPR: What relevant biological changes in immune cells and ovarian cancer cells could be detected in KIYATEC’s platform after treatment with immune checkpoint inhibitors
MG: From the clinical perspective, tumor cell death is the most important biological change after treatment with immune checkpoint inhibitors. This has the potential to provide evidence of whether a drug regimen will lead to a clinical response in a patient.
As opposed to a tight focus on tumor cell death, pharmaceutical companies value knowing biologically relevant changes in immune cells after treatment with checkpoint inhibitors alone or in combination with other inhibitors. For example, T cell cytokine production, Treg suppressive function, and antigen presenting cell activity are just some of the immune components which can be assessed in our platform. These changes help inform companies of a drug’s mechanism of action and can provide evidence of which inhibitor or combination of inhibitors is superior in different cancers.
BPR: In what way then can pharmaceutical companies use KIYATEC's platform to aid decision-making during drug lifecycles?
MG: KIYATEC’s platform can be used to collect clinically meaningful information about drug-induced changes in both the tumor and tumor-infiltrating immune cells in a patient’s tumor. Pharmaceutical companies can use this information for evidence-based decision making regarding which drugs should be advanced in their development pipelines. The ability to do this using the existing models is limited, as many current methods have limited relevance to patient biology, or rely on statistical probability, versus individual tumor evidence of treatment response.
BPR: How far off is KIYATEC in having a platform that better predicts which patients will respond to immunotherapies?
MG: We have defined the four steps that must be achieved to develop a platform for the prediction of patient response to immunotherapies. We have accomplished the first three steps: 1) develop a live cell co-culture test with patient-matched cells, 2) demonstrate sustained functionality of key immune cells in the co-culture, and 3) characterize dose-dependent and patient-specific cellular responses. We are now just one step away from achieving this goal; the final step is to establish predictive accuracy by correlating test results to patient clinical outcomes. The timeline for this final step is dependent upon resourcing and clinical study-specific factors and has yet to be determined.
