The Immunotherapy Challenge: Moving from Preclinical Research to Clinical Practice
By Ralf Huss
September 18, 2014 | Contributed Commentary | We are currently witnessing a paradigm shift in cancer diagnosis and treatment. After decades of harsh radiation and chemotherapy treatment for most tumors, researchers and clinicians are getting serious about personalized medicine. It has been a rough road so far with only a few examples of successful patient stratification, which sometimes predicted a desired response in less than 25% of patients and eventually led to therapy resistance. But the promise is there and now oncologists and the pharmaceutical industry are embracing the cumulative understanding of years of cancer biology study and are following new paths in cancer treatment. Although some of these paths — such as adaptive immunotherapy — aren’t new anymore, we finally have the necessary tools to understand and translate the complexity of an individual’s disease into a personalized care plan.
Recent research in immunotherapy has revealed the prognostic and predictive value of a patient’s immune status. Studies in colorectal cancer led by prominent immunologist Professor Jérôme Galon indicate the significance of a patient’s immune status for determining clinical outcomes and long-term treatment success. Immune status is derived from a complete analysis of the number, type and location of tumor infiltrating lymphocytes in the tumor microenvironment. Historically these aspects of the tumor microenvironment have been difficult to accurately measure, but image analysis is now advancing to the point that it is possible to quantitate immune cells in specific locations of a cancer tissue specimen. Research suggests that in addition to predicting the likelihood of tumor recurrence, immune status is an invaluable tool for identifying predictive biomarkers and improving patient stratification in clinical trials, both of which are needed for the development of effective immunotherapeutic treatments.
Big Data Provides the Solutions
The availability of big data extracted from a patient’s cancer tissue, in combination with the patient’s personal history and other available information such as genetic risk factors, demographic trends, and accessible therapies, will provide a meaningful basis for the adequate management of an individual’s disease. This, however, requires the access to, and the storage and handling of, many big data correlations in order to provide the long term options that have the best impact for the individual patient.
A good example of the emergence of new immune therapies in combination with big data analysis is the FDA approval of ipilimumab for the treatment of metastatic melanoma in 2011. This has been followed by development of checkpoint inhibitors that target CTLA-4 pathways, which induce the body’s immune system to recognize and respond to cancer without triggering an autoimmune response. In addition to CTLA-4 inhibitors, exploration of PD-1 and PD-L1 pathways has gained significant ground as an alternative method of mitigating the ways in which cancer takes advantage of the immune system’s natural checkpoints to silence the body’s immune cells. In spite of impressive results in these studies, determining a patient’s individual response to treatment remains a challenge. However, a new approach, using quantitative image analysis for the identification of all relevant prognostic and predictive tissue biomarkers, is promising to the advancement of these types of immunotherapies.
The Path to Widespread Adoption of Immunotherapies
Researchers at the clinical level continue to work diligently to discover why some cancer patients respond to specific types of immunotherapies while others do not. In order to do this, it is of crucial importance to identify the best and most robust cancer biomarkers. Tissue analysis can be a promising route to discovering these biomarkers, but only if researchers have access to standardized tissue specimens and can extract many important data points derived from different cells types (cancer vs. immune system), blood vessels, stroma components, and signs of previous treatment in tissue specimens. Finally, it is the quantitative understanding of these components and their spatial distribution in a multidimensional space that will enable the best biomarkers to be discovered and applied clinically to stratify patients for immunotherapy treatment. This ultimately requires a reproducible procedure for how tissue diagnostics and analysis is done. At Definiens, we believe that investment into a diagnostic approach that performs “tissue datafication” and correlates tissue markers with other data sources is a valuable investment into patient care related to outcome and quality of life.
The widespread adoption and long-term success of specific immunotherapies will depend upon treatment decisions that can be clinically substantiated. With the right tools now available to identify effective tissue biomarkers, there can be little doubt that the field of immunotherapy is poised for significant growth in 2015 and beyond.
Dr. Ralf Huss is Chief Medical Officer at Definiens. He has more than 20 years of training and experience in histopathology and cancer research. In his role as Chief Medical Officer, Dr. Huss plays a key role in expanding Definiens’ image and data analysis into tissue diagnostics and clinical digital pathology. Dr. Huss holds global academic appointments at the Ludwig-Maximilians-University, Munich and the Wake Forest Institute for Regenerative Medicine, USA.