MARKERS THAT CORRELATE WELL WITH CANCER
There are examples of markers that correlate well with the disease and can be used to predict clinical events in advance of clinical symptoms or obvious scan changes. Examples of such markers include PSA in prostate cancer, CA125 in ovarian cancer, and AFP and HCG in testicular cancer. Even when good markers exist, they cannot necessarily replace other clinical methods of assessment. For example, while changes in PSA largely reflect changes in disease status, some treatments that affect the clinical outcomes (the bone- hardening drugs called bisphosphonates are a good example) have a very little effect on PSA levels despite helping prevent bone damage by cancer. Even more surprisingly, a recent huge study in ovarian cancer and the use of markers produced very counter-intuitive results. A rising level of CA125 in the blood accurately predicts clinical relapse. One might expect that treating relapse early would be better than waiting until symptoms developed. The study compared a policy of marker-driven treatment (that is, treatment for relapse started with rising marker levels) with clinical symptom-driven treatment. A total of around 1,500 women took part in the study, and the earlier introduction of treatment in women with more intensive monitoring did not affect survival times. Even more surprisingly, quality of life and anxiety levels were better in the women with the clinically driven treatment – tighter monitoring and earlier treatment were actually therefore inferior overall.
A huge focus of current research is individualized therapy – identifying markers that allow treatment to be tailored to the individual. There are many ways tumours can be characterized – by their DNA mutations, by their patterns of protein expression, by looking at the activities of different enzymes. However, while it is relatively easy to identify patterns that correlate with different outcomes, it will be obvious from the above discussion that this will not be sufficient to allow treatment to be altered. To demonstrate clinical value will require clinical trials comparing the candidate marker-driven policy with standard care. As the ovarian cancer example above shows, even having a good marker does not guarantee the expected result. A further problem that may emerge is that the numbers of candidate markers being developed may exceed the capacity of research teams to carry out trials, possibly many times over. Furthermore, markers effectively change a disease from being a homogenous entity to a number of distinct subentities. As good trials need large numbers, this makes doing trials more difficult – the disease effectively becomes rarer. This is illustrated by recent changes in renal cancer. A number of pathological variants had been described some time ago but until the advent of targeted small molecules, this made no difference to treatment options. The abnormalities in clear cell renal cancer (around 70% of the total) lead to new treatments. What then for the remaining 30%? Several different further subtypes make up this 30%, hence trials now become difficult as each is really rather uncommon. As a result, we don’t really know how to manage these subgroups. These so-called ‘orphan’ diseases will become increasingly common and problematical as there will be little in the way of trial data to inform treatments, and trials will be difficult due to lack of numbers.
The coming years will see many exciting developments in new cancer drugs, new biomarkers, and exciting and futuristic technology such as surgical robots. How we incorporate these developments in practice will depend in large measure on clinical research to underpin their use. However, new technologies, in particular, will have a tendency to be introduced via the marketing rather than trials route. How we license, regulate, and fund these devices will become increasingly problematical as healthcare budgets come under pressure with an ageing population and the massive debt overhang of the credit crunch.