Harnessing the immune system to attack tumours has led to some of the most promising new treatments for cancer. Despite the exhaustive ongoing research to assess diagnostic, predictive and prognostic immunotherapy biomarker candidates, only a few have reached the point of use in the detection and management of cancer. Moreover, even approved biomarkers such as programmed death-ligand 1 (PD-L1) have only proven useful in limited circumstances, as it is just one element in the complex pathways involved in immune escape.
Mario discusses how to choose the right biomarker, and the future of research in this field.
Q: How can you ensure that you’ve chosen the right biomarker?
A: Choosing the right biomarker is a huge challenge, and developing immuno-oncology trials requires thoughtful planning. The body’s systems can undergo many changes in disease states, some of which may not be tied to the primary pathophysiology of a disease. As a result, focusing on the wrong biomarker can lead a trial astray.
For example, studies have demonstrated inconsistencies in using PD-L1 as a predictive biomarker for cancer immunotherapy. While tumor PD-L1 expression does correlate with poor prognosis in a few cancers, it also correlates with a better prognosis in others. Moreover, factors such as the time of tissue sampling, effects of other treatments such as chemotherapy, and varying PD-L1 expression levels in tumours at different locations can confound the interpretation and validity of PD-L1 results.
PD-L1 is not unique in its complexity. Therefore, disciplines such as pharmacometabolomics, which quantifies and analyses metabolites produced by the body, can be key in determining a biomarker that correlates best with a primary disease process.
Q: What other challenges does your team work to solve in immuno-oncology?
A: In immuno-oncology trials, a big challenge is pseudo-progression, where tumour size increases compared to baseline assessment measurements. This may occur when T-cells infiltrate the tumour site and cause them to inflame, which may prolong tumour shrinkage. Therefore, physicians must be trained to evaluate a true progression versus a pseudo-progression. Ways to differentiate the two are detailed in the ICON white paper entitled, Setting the Scene in Immuno-oncology.
Working with experienced sites becomes more critical when taking these factors into consideration. ICON has the experience and expertise to address these challenges with sites, and thus keep early unnecessary patient withdrawals to an absolute minimum. Implementation of immune-related response criteria is essential for the assessments of the true efficacy of a cancer therapy.
There also are more general challenges we face, such as keeping specimens stable under sample storage conditions, especially for late-phase clinical studies where multiple sites and investigators are involved. Work needs to be done to ensure that samples maintain their integrity under storage and shipping conditions. If this is not performed properly, then we run the risk of erroneously measuring the integrity of the biomarker in our samples, as opposed to the effect the drug candidate has in treating disease.
Even though these challenges remain, we are making significant progress, as exemplified by the development of tumour infiltrating lymphocytes (white blood cells that have penetrated a tumour as part of the body’s immune response) as a predictive and prognostic biomarker in certain forms of breast cancer, in addition to advances we’re seeing in imaging technology. With further research, we hope to surmount the challenges to date, and to increase our success in developing and deploying biomarkers of all types to ultimately improve patient outcomes.
Q: Where do you see the future of biomarkers headed? How will genetics shape that future?
A. Over the past several years, with advancements in gene sequencing technologies and the growing knowledge of biomarkers for many diseases, we’ve come to realize that we were developing drugs in a “one size fits all” manner for diseases that are in fact heterogeneous. We now know the optimal treatment of a patient may require evaluations that determine the underlying causative factors for their subtype of a disease.
In the future, we would like not only to see biomarkers for reliably diagnosing disease, but also for determining which individuals are predisposed to develop a certain form of a disease; for identifying the types of therapy that will be successful in treating a disease; and for characterizing the prognosis for a patient.
To reach these future ideals, we need a better understanding on the heterogeneity of diseases from a genetic and epigenetic viewpoint. Also, we have to advance mass spectrometry, imaging and immunologic analytical technologies to allow for proper analysis and discrimination of biomarkers.
Moreover, as the cost of sequencing a person’s genome continues to drop, there will be incredible insight, knowledge and advances made when this genomic information and accompanying patient-specific data is mined. Uncovering trends and correlations between a person’s genetic make-up, their disease profile and outcomes will be key in future uses of biomarkers in drug development and diagnosis and therapeutic management of disease.
ICON’s imaging group has developed and used special immune-related set of criteria extensively over the last few years to accurately assess response assessments in immuno-oncology trials.
In conducting nearly 80 immuno-oncology trials to date, ICON has been immersed in the development of both passive and active immunotherapy approaches, allowing sponsors to overcome challenges in immuno-oncology.
Oncology insights
ICON's Oncology experts provide analysis including whitepapers, blogs and contributions to media and industry conversations relating to all aspects of oncology in clinical trials.
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Digital Disruption
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Clinical strategies to optimise SaMD for treating mental health
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Digital Disruption: Surveying the industry's evolving landscape
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Clinical Trial Tokenisation
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Closing the evidence gap: The value of digital health technologies in supporting drug reimbursement decisions
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Digital disruption in biopharma
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The triad of trust: Navigating real-world healthcare data integration
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Clinical strategies to optimise SaMD for treating mental health
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