Cancer immunotherapy certainly isn’t a new concept. The hypothesis that we can spark tumor regression by stimulating the immune system is as old as the late 1800s. However, 2014 saw FDA approval for two cancer immunotherapies, Keytruda and Opdivo, throwing the scientific community into a two-year honeymoon of somewhat inflated expectations.
With the recent discontinuation of clinical trials for expanded use of BMS’s Opdivo and the hiccups in Juno Therapeutics’s CAR-T therapy clinical trials, the honeymoon for cancer immunotherapy has come to an end.
What does this mean for the progression of immunotherapy as a means of treating patients?
The honeymoon may be over, but that doesn’t change the fact that cancer immunotherapy is the biggest discovery in oncology in the last decade or two. To continue making progress in immunotherapy, there must be improvement in two key categories—clinical trial design and companion diagnostics (CDx).
Why Patient Selection and Eligibility are Major Hurdles
Because advancements in cancer immunotherapy are fairly new, there are multiple challenges that researchers must overcome before treatments are commercialized.
For example, choosing clinical endpoints has been a difficult process for researchers because of the differences between immunotherapy and non-immunotherapy drug kinetics. Applying traditional clinical endpoints—reduced tumor size, significant overall survival rate, progression-free survival, etc.—to immunotherapy clinical trials could hinder drug approvals.
While there are other examples of challenges that face immunotherapy clinical trials, the most significant is personalization and diagnostics. We’re entrenched in the era of personalized medicine. Because of personalization trends, there’s a need to meet growing demands for precision targeting, regardless of whether cancer immunotherapies are developed as monoclonal antibodies, immune checkpoint inhibitors, or vaccines.
The problem with immunotherapy personalization and diagnostics is the distinction between biomarkers and targets in clinical trials. Biomarkers are essential to determining which patients are most likely to benefit from the immunotherapy drug that is being tested. However, the connection between a predictive biomarker and an effective therapeutic target is not always clear.
This has been the challenge with the popular PD-1/PD-L1 biomarker. It was a breakthrough in patient targeting, but hasn’t been as effective as we originally thought. Because PD-1/PD-L1 expressions are ubiquitous, predictions about a drug that targets the biomarker can vary based on tumor size. This makes standardization difficult.
These issues are not exclusive to the PD-1/PD-L1 biomarker. Other options such as CTLA-4, Tim-3, LAG-3, and others have also proven difficult to match patient predictions with strong precision targeting.
Whether biomarkers are strong predictors but weak targets or vice versa, the fact remains that clinical researchers must overcome this challenge before cancer immunotherapies can be commercialized. Pharmaceutical companies should consider making this distinction as well in their efforts to find new targets.
Where Companion Diagnostics Fit into Cancer Immunotherapy
New companion diagnostics will play a vital role in overcoming the inclusion/exclusion challenges in patient selection and eligibility. Traditional standards and tools can’t keep up with the speed and sophistication necessary to succeed with cancer immunotherapies.
For example, we no longer have to limit cancer diagnostics to needle biopsies. Instead, a number of different biofluids can be used to detect tumor mutations—blood, cerebrospinal fluid, urine, stool, saliva, and more.
These biofluids can be leveraged to gain quantifiable measurements in situations where needle biopsies won’t work or are dangerous. Specifically, liquid biopsies can be used in cases of brain tumors or internal organ tumors, enabling blood testing to convey information about levels of mutation, cancer origins, and more.
According to the FDA, CDx can be leveraged to:
- Determine which patients are most and least likely to benefit from a specific immunotherapy and personalize how and when the drug should be used.
- Identify the patients who are at greatest risk to experience adverse side effects that can jeopardize both patient safety and the clinical trial.
- Monitor response to immunotherapies effectively and adjust treatment for safety and effectiveness as necessary.
Ultimately, CDx can improve patient safety while also helping companies choose exclusion and inclusion criteria for clinical trials more effectively. Improving exclusion and inclusion criteria for cancer immunotherapy trials will drastically increase the likelihood of success of a clinical trial.
The Honeymoon is Over, But the Relationship is Just Beginning
The overinflated expectations surrounding cancer immunotherapies have been quieted to some extent—but we’re just getting started with immunotherapy innovation.
As the immunotherapy industry evolves, we’re starting to see a more discernible value chain. Like any other industry, this value chain is made up of the various elements that ultimately affect the end goal—speedy commercialization of safe and effective cancer drugs.
We know that diagnostics and manufacturing are important pieces, but more sectors will become apparent as the path from ideation to commercialization of cancer immunotherapies becomes clearer.
As the pieces of the value chain come into focus, pharma companies must be able to join forces with the small- and medium-size businesses that are innovating around biomarker identification, target identification platforms.
The problem for big pharma is that staying on top of the entire landscape of immunotherapy innovation is a massive undertaking, especially as more and more players enter the market with innovative solutions. Effective partnerships, merger and acquisition targeting is critical—but far from easy.
If you want to learn how a data intelligence platform can help you get ahead of the cancer therapy curve and optimize your business strategy, contact us today for a free demo of Signals Playbook™.
Written by Priya Kar
Priya Kar is a Life Sciences Customer Success Manager at Signals Analytics, a Decision Science as a Service company, that enables global organizations to continuously experience the “aha moment” through Signals Playbook™, a cloud-based analytical intelligence platform that transforms the world’s unconnected data into actionable insights to enhance customer experience, optimize product portfolio health and propel innovation. She has a Ph.D. in Molecular Biology from New Jersey Medical School and an MBA in Marketing and Pharmaceutical Management from Rutgers Business School. She has a strong academic background in Oncology along with pharmaceutical business experience in Strategy and Operations. She has co-authored several peer-reviewed scientific publications and has received many awards for her academic performance as well as business acumen.