Immuno-Oncology – Good Today, Great Tomorrow
May 2nd, 2016
Posted by Shane Climie, Ph.D.
Researchers in the field of immuno-oncology have achieved remarkable success over the past five years. The ability to boost the immune system to attack cancer using a variety of biotechnology-derived approaches has led to the successful treatment of thousands of patients with advanced melanoma, lung cancer, and various types of leukemia. The success of these approaches has extended the lives of many patients, a significant number of whom have seemingly been cured. All of this has created a great deal of excitement and optimism among patients, researchers, and physicians after many years of frustration, false starts, and failed trials.
Cancer cells are tricky – they carry cell surface proteins that are recognized by the immune system as “self”, thereby evading attack and destruction by T-cells and other effector cells that normally confront “non-self” invaders such as bacteria and viruses. Immuno-therapies seek to overcome the ability of cancer to evade the immune system – thereby provoking an attack on what would otherwise be an invisible target.
Three general approaches have been adopted:
- Antibody-based therapies that modulate the immune response include various checkpoint inhibitors that act to release the brakes of the immune system.
- Adoptive T-cell therapies, including chimeric antigen receptors, that carry engineered T-cell receptors that guide those cells directly to an otherwise invisible tumor cell, and
- Cancer vaccines that boost the immune system – akin to stepping on the gas.
However, despite the remarkable success stories associated with these approaches, there are nagging problems. While some patients achieve remarkable outcomes, a large number do not respond especially well – as few as 4 in 10 patients may benefit from certain types of immuno-therapy. Also, side effects can be severe and life threatening, often mimicking aspects of autoimmune disease – including rash, inflammation, and damage to the endocrine system.
As such, great efforts are underway to improve immuno-therapy through a better understanding of the underlying molecular basis of cancer and the immune system.
The landscape is complicated, and many factors can affect outcomes – such as the presence of specific mutations, the overall mutational burden associated with a given tumor (resulting in the presence of neoantigens that act as targets for the immune system), the relative levels of various immune effector cells, the presence of cytokines, and complex biology associated with the tumor microenvironment.
Several technologies are being used to in order to gain a better understanding of the underlying biology. These include the use of fluorescence activated cell sorting (FACS) for immune profiling, the generation of gene expression signatures that correlate with clinical outcomes, the use of other predictive biomarkers, NGS-based immune profiling and mutation detection, the identification of tumor specific antigens, the use of immuno-histochemistry to detect the presence of cellular biomarkers – the list goes on.
The Road Ahead
The information generated using these technologies will enhance the clinical utility of existing immuno-therapies by enabling the development of Dx tests to identify those patients who are most likely to respond, monitoring patients who are undergoing therapy, and providing support for the development of novel therapeutic agents. Although individual markers such as PD-L1 are now being used as companion diagnostics for checkpoint inhibitors, their utility is limited.
A successful immuno-oncology Dx strategy will need to interrogate several aspects of the biological landscape – including activation of the immune system, the presence of specific tumor antigens, and the tumor microenvironment, among others.
The underlying complexity of immuno-oncology demands a Dx strategy that goes beyond the typical 1-to-1 logic of companion diagnostics. What’s needed is a Dx strategy that predicts clinical benefit based on the application of 1-to-many logic. This will likely be based on the identification of multiple markers and correlations with various outcomes. This approach will require the integration of multiple data types, a consideration of both genotypic and phenotypic information, test panels, and high content analysis.
In short, this approach will help to propel the field of immuno-oncology from good to great.
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