The phrase “bench to bedside” describes the conventional model of cancer drug development. It’s the tried-and-true approach — a linear path that starts with basic research and ends with clinical trials. But we may need a more dynamic process for the emerging science of cancer immunotherapy, an approach to fighting cancer that harnesses the power of the body’s own immune system.
“Given the immense potential of cancer immunotherapy, we are initiating clinical trials in this area at an unprecedented pace,” notes Dan Chen, cancer immunotherapy franchise head. “In fact, the rate at which we are collecting clinical trial data in cancer immunotherapy is now outpacing our understanding of the underlying biology.”
That surge in data means that clinical trials, normally seen as the final test of a well-studied therapeutic approach, could actually open the door to new areas of immunotherapy research. For example, a particular form of cancer might not respond to an investigational immunotherapy in clinical trials, even though preclinical results suggested it should. In the conventional “bench to bedside” model, the research might end here. But with cancer immunotherapy, scientists might identify specific features of the tumor— features that could have stymied the immune response and made it vulnerable to a new approach—and send those back to the lab for further study.
According to Priti Hegde, cancer immunotherapy franchise biomarker lead, “this knowledge gained in the clinic can now help design new preclinical experiments in mouse models that may more closely mimic the human disease. In this case, preclinical animal work and human clinical data work together to help fill in scientific gaps that could lead to new investigational therapies and combinations.” And given the way we approach cancer drug development, our scientists are in a unique position to turn the conventional model on its head.
“Our clinical, research and biomarker teams have all been working very closely together on cancer immunotherapies from the outset,” says Matthew Albert, principal scientist of Cancer Immunology. “This approach allows us to rapidly take clinical insights back to the lab to try to understand why certain cancers didn’t respond to therapy. We refer to this process as ‘reverse translation’ - using real-time human clinical data to directly inform new discoveries.”
A study in the journal Nature published on February 14, 2018 illustrates the power of this approach. In a clinical trial of one of our cancer immunotherapies, our scientists observed differences in people who responded to the medicine. By digging deeper into the biology of the non-responders, the team discovered that some of them had an up-regulated gene signature associated with a protein called TGF-beta. With this knowledge, they then explored the phenomenon in pre-clinical models. When they combined the cancer immunotherapy with an investigational antibody that blocks TGF-beta, it resulted in improved anti-tumor activity in pre-clinical models that mimic the biology of some non-responders. These results are a perfect example of reverse translation, and of how we may be able to bring the benefits of cancer immunotherapy to a larger number of people.
The path forward for immunotherapy may not be the traditional line from bench to bedside, but instead a feedback loop in which each step continuously influences the other.
When all of that comes together it looks like this:
But let’s break it down step-by-step to better understand the process:
At the Bench
The Cancer Immunity Cycle helps guide preclinical studies that identify candidate immunotherapies or combinations. Selection of a biomarker may help determine people most likely to benefit from those therapies.
Entering Clinical Trials
When enough preclinical evidence has been gathered, the clinical trial process with the targeted population begins.
Evaluating Human Clinical Data
For those who respond in the trial, a new path towards drug development and personalized health care begins. But for those who did not respond, their unique biology is investigated back in the lab to determine why not.
Generating New Hypotheses
By assessing the clinical data, the biomarker and clinical teams evaluate how that specific tumor evolved and generate hypotheses to explain why it did not respond to treatment.
From Bedside Back to the Bench
The research team takes the insights from the clinic and uses these results to design preclinical animal experiments to test their hypotheses, using the Cancer Immunity Cycle as a guide. They modify their targets and the process starts again.
This article was updated on 2/21/2018 to include information from the 2/14/2018 publication in Nature, "TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells."