Why we must work out why some people respond exceptionally well to cancer treatments. Phil and Pam Gradwell (to be)/Flickr, CC BY-SA
Most cancer specialists – indeed, most doctors – have treated patients who respond to treatment in unexpected ways. Known as “exceptional responders”, the experiences of these patients and their doctors are generally relegated to anecdotes. While it may have once been impossible to tell why they responded better to treatment than others, aneditorial in Science Translational Medicine argues that technology is now so advanced that we could actually learn something from such patients.
For example, most patients with pancreatic cancer do not respond to treatment and survival rates have remained below 5%. Yet every so often a patient has a dramatic response to chemotherapy and the cancer stays away for years. Considering the average survival for pancreatic cancer is about six months, this is exceptional.
Rapid recent advances in genomic sequencing is allowing us to dig deep into the biology of individual tumours – and other diseases – and perhaps understand the genetic basis of positive responses. Indeed, understanding this – which particular subtype of a cancer responds well to a particular therapy, for example – paves the way for better targeted treatments for individual patients. Often called precision oncology, this personalised treatment is the emerging model of high-tech cancer care.
The challenge lies in how we establish the link between the underlying biology of a particular cancer and the patient’s response to treatment. This is particularly important since traditional statistical methods can’t help when dealing with the enormous diversity between individuals. Perhaps an even greater challenge is the way in which details of these rare events can be compiled in order for us to glean some insight and apply what we’ve learned to improve medicines and treatment in a meaningful way.
The need for networks
The authors, Eric Perakslis and Isaac Kohane of Harvard Medical School, suggest we should follow the example set in the study of rare diseases, where similar challenges have been overcome through forming global research networks. They propose founding a special network that can bring together data about exceptional responders. This would allow clinical sites to deeply analyse a certain tumour and connect it with clinical information.
A registry of exceptional responders could pull together data on the patients and the finest genetic details of the cancers involved. This would help us work out what meaningful similarities and differences there are. Of course, it is essential to share this data within and outside the network for greater benefit, in an ethically acceptable way.
This is a step forward – the importance of the societal shift in the way we approach healthcare cannot be underestimated. We now understand more and more about disease. We’re beginning to realise that what we once thought was a single disease (basing this on a microscopic analysis) reveals dramatic differences in the genetic composition of tumours. There are multiple genetic subtypes of cancer – even when they appear similar under the microscope.
Over the longer term, the difficulty will grow. New therapies will become increasingly sophisticated and able to directly target a cancer’s biology. A treatment that targets a particular subtype of cancer will most likely not work when given to patients that do not have that subtype of cancer. As we understand more, we’ll classify more subgroups of cancer. These subgroups will become smaller and smaller and it will become impossible to perform clinical trials of sufficient size. This means it may take many years to recruit enough participants in order to properly test a particular therapy.
Imagine what we will need in the future. Rather than a series of clinical trials to test only a small fraction of possible hypotheses, we will need to move toward what’s known as a “knowledge-bank approach”. Faced with particular genetic subtypes of cancer for which there are few if any existing clinical trials, being able to turn to a knowledge bank rich in data on patients, their tumours, treatment and responses could become the guide for selecting the right therapy in the future.
This data-driven approach starts with the national and international networks and patient registries the paper’s authors describe. With sufficient data – and a proper framework for sharing that information among experts – it’s possible that, in the future, every patient will show an exceptional response to their treatment.
This article originally appeared on The Conversation.