Blood cancers

When do we manage CLL as a chronic disease and when do we aim for cure?

Wednesday, 14 Nov 2018

Developing tools that enable clinicians to predict which subgroups of patients can be cured and which will require ongoing treatment is critical in optimising outcomes in patients with chronic lymphocytic leukaemia (CLL), Professor Peter Hillmen, Haematologist at St James University Hospital in Leeds, UK, told delegates at the Janssen H3 conference held in Sydney last month.

How can mathematical modelling help guide patient care?

During his presentation entitled, ‘What is the treatment goal in CLL: manage as a chronic disease or aim for a cure?’, Professor Hillmen explained that mathematical models have the potential to aid in treatment design, trial analysis and also to inform the appropriate selection of therapy in blood cancers.

For example, he described a model developed by his colleague Professor Walter Gregory quantifying the mechanisms and dynamics of tumour growth, cell-kill and resistance as they affect durations of benefit after cancer treatment.[1]

This model demonstrates that the interrelationships between biological factors of the primary tumour and treatment can be determined, in a structured and logical manner, via the effects of treatment on resistant sub-clinical tumour burden and growth rates (measured by log doubling rate of the tumour from the time that disease is detectable to when the patient relapses clinically).[1]

In this way it may be possible to identify the population of patients who, even with effective therapy, are destined to relapse early versus later as well as those who are destined to be cured.

The evolving role of MRD

According to Professor Hillmen, the use of minimal residual disease (MRD) as a surrogate for clinical outcomes such as progression-free survival and overall survival will become increasingly important as we move towards more effective treatments.

He explained, “It can take randomised controlled trials many years to evaluate progression-free survival or overall survival… We can’t wait 7, 8, 9, years for a PFS to see if the treatment has been effective… we have to use early surrogates such as MRD”.

Another area where MRD may be useful is in determining duration of therapy. In light of this, novel/novel or novel/anti-CD20 combination trials have been designed to investigate MRD guided treatment durations.

While these trials should help elucidate whether MRD is a useful tool for determining appropriate duration of therapy, there is no clear evidence as to what level of MRD is required for disease eradication and the sensitivity of MRD measurement is continually evolving.

Advances in the treatment of CLL – what does the future hold?

Professor Hillmen highlighted that advances in CLL are happening incredibly quickly and there is a need to design clinical trials linking our understanding of the biology of CLL to its treatment. He explained that linking Phase I/II and Phase III programs will allow for rapid acceleration to combination therapies in the front-line setting. In addition, there is a critical need for biobanking and translational research as well as detailed health economic analyses in large trials.

He explained, “We have to generate information that will allow us to use these therapies when we move out of clinical trials.”

Professor Hillmen also noted that because chemotherapy can cause damage to patients with high-risk characteristics and make their disease more resistant, there is an increasing need to be able to identify which patients may be more suitable for front-line targeted therapies.

He concluded by suggesting that in the future, novel agents will be available for the first-line treatment of CLL that result in markedly lower rates of treatment failure. Ideally, combinations of novel agents will enable a defined treatment period that is either fixed or driven by MRD and biological markers, such as IGHV mutation status, will be used to guide therapy choices, thereby optimising outcomes in our patients with CLL.



  1. Gregory WM, et al. Characterizing and quantifying the effects of breast cancer therapy using mathematical modelling. Breast Cancer Res Treat 2016;155:303–311.

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