Patients’ varying responses to treatment are one of the main reasons cancer is difficult to treat. These variations are rarely anticipated. The majority of the time, administering a therapy to a patient and then waiting and watching is required to determine whether or not the patient will respond to it. That puts a lot of pressure on doctors and researchers, poses a lot of danger to cancer patients, and costs more.
The most effective treatments and regimens could be tailored to each individual patient, thereby increasing the likelihood of success. Because of this, researchers have been concentrating on finding useful biomarkers for tumors. A biomarker is a collection of biological molecules produced by a cancer patient’s tumor with the intention of revealing the tumor’s formation, behavior, and, hopefully, response to a specific treatment.
Existing biomarkers have led to tests with encouraging but limited reliability
Tests based on existing biomarkers have produced promising results with limited reliability. Immune checkpoint inhibitor (ICI) therapies are one of the most significant recent advancements in cancer treatment. ICIs function by preventing partner proteins from binding to checkpoint proteins for a sufficient amount of time to permit T cells in the body to eradicate cancer cells. This is important because the tumor tricks the immune system into turning itself off in all types of cancer, allowing the cancer to grow. Sadly, the majority of cancer patients do not respond to these medications. It is essential to determine who will benefit and who will not.
Our study on the prediction of ICI treatment is not the first of its kind. Predicting how tumors will respond to ICI therapies has already been tested, and the results are encouraging. However, these earlier tests had some or all of the following limitations: limited patient applications, low accuracy, and complicated protocols. Specifically, no previous tests have predicted negative outcomes.
Lung cancer is the leading cause of cancer deaths and we’re looking for a solution
We are looking for a solution because lung cancer is the leading cause of cancer deaths worldwide. Our team chose to initially concentrate on lung cancer because it is the leading cause of cancer deaths worldwide. Every year, more than 2 million people are diagnosed with lung cancer, and another 2 million die from the disease, many of whom are receiving treatment.
Lung cancer is currently being treated primarily with ICIs. They have demonstrated to be apparently marvel drugs for some, yet convey critical drawbacks like extremely significant expenses, chance of serious unfriendly secondary effects, and critically, low understanding reaction rates. That was the major issue we wanted to address and still do: how to use ICIs to treat cancer more effectively, less expensively, and without or with fewer side effects.
Current research revealed a new class of biomarkers for predicting treatment outcomes
A new group of biomarkers for predicting treatment outcomes was discovered in recent research. The Calviri team discovered a new group of biomarkers for predicting these treatment responses and adverse events. In a recent study that was published in the Journal of Translational Medicine, we demonstrated the biomarkers’ usefulness for predicting outcomes in lung cancer patients receiving ICI therapy regimens.
Our exploration began with blood tests drawn from 74 patients determined to have advanced cellular breakdown in the lungs. These patients received either chemotherapy or immunotherapy against PD-L1 or PD-1 following the blood draws. We created a blood-based, or liquid biopsy, test based on these data that accurately predicted these patients’ post-treatment tumor responses and adverse events.
Anti-FSP antibodies, also known as blood-based biomarkers, are the foundation of this new test. During RNA processing, such as splicing, we found that tumors make a lot of mistakes that translate into variant, out-of-frame peptides. These frameshift peptides are extremely immunogenic, which means that they elicit an immune response and produce antibodies that are simple to detect in our tests.
This is exciting news because the information contained in these biomarkers may aid in the development of improved or orthogonal testing to predict how cancer treatments will affect any tumor. Our test will be inexpensive and applicable to a wide range of situations because it uses only a small amount of blood in a straightforward assay. We expect to empower doctors to anticipate treatment results and make precise, ideally protected, and powerful treatment proposals for every patient.
These newly discovered biomarkers could have implications beyond lung cancer
Even though our team was looking at lung cancer, the findings of this study and the ones it produced are applicable to other types of cancer as well. These newly discovered biomarkers may have applications that go beyond lung cancer. Many types of cancer that typically do not respond to ICI therapies could benefit from the new biomarkers we discovered. For instance, the overall ICI response rate for brain cancer is extremely low. Consequently, ICIs are not offered to brain cancer patients, even though some of them would have responded. Physicians might be able to identify these patients through a straightforward test that makes use of anti-FSP biomarkers, allowing them to provide treatment and save lives.
Conclusion
Despite the fact that this study addresses the most important phase in anticipating results for immunotherapy, it is a significant positive development. Our next objective is to collect and analyze data from a larger sample and use that data to further develop this new technology. Importantly, we are concentrating on broadening the scope so that we can both assess its potential application to the treatment of other cancers and chronic diseases and address the particular requirements of lung cancer treatment.
More information: Luhui Shen et al, Predicting response and toxicity to immune checkpoint inhibitors in lung cancer using antibodies to frameshift neoantigens, Journal of Translational Medicine (2023). DOI: 10.1186/s12967-023-04172-w
