Reducing the Symptom Burden Produced by Aggressive Cancer Therapies
We propose clinical trials that will assess the effectiveness of using a combination of therapies to
reduce symptom burden during aggressive cancer treatment. The target populations for study are
patients undergoing chemoradiotherapy for head and neck cancer or lung cancer. The high symptom
burden caused by aggressive treatment of these cancers may become so severe that patients may be forced to
interrupt therapy, may need to make unscheduled visits for emergency symptom care, and/or may have
difficulty maintaining vocational and family commitments during therapy and for weeks thereafter.
The combined interventions we will test are derived from a theoretical model based on evidence that
treatment-produced inflammation is a major cause for many symptoms, such as fatigue, appetite loss,
emotional distress, and pain, and that modulation of this inflammation and its consequences will significantly
reduce symptom burden. Combinations of therapy will include inhibitors of NF-[unreadable]B (a precursor molecule for
inflammation), inhibitors of inflammatory cytokines, and an antidepressant (bupropion) that also has
inflammatory action. We will include a wakefulness-promoting agent (modafinil) that may modify the effects of
inflammatory cytokines on the brain.
We will utilize new methods of assessment (a computer-based telephone assessment system combined with
symptom-assessment questionnaires developed for each disease condition) to frequently monitor the severity
of treatment-specific symptoms over the time of the study. Symptoms will be monitored twice a week using the
telephone-based assessment system. This method will allow us to derive an area under the curve (AUC) for
multiple symptoms for each patient group across the period of treatment. Changes in AUC for established
severe treatment-related symptoms for each group will be the primary trial outcome variable.
A key component of this proposal is the use of a Bayesian adaptive design to rapidly assess the efficacy
of the several treatments and treatment synergies. Such a design take advantage of accumulating trial results
by assessing them periodically;the trial can then be adjusted by slowing (or stopping) ineffective interventions
or by expanding patient accrual to better-performing therapies, potentially leading to smaller, more
informative trials and better treatments for patients. With now-available increases in computational power and
newer modeling methods, these adaptive methods are increasingly being used in clinical research for curative
therapies but have yet to be used in symptom research, where their advantages in maximizing
treatment benefits within the trial period are attractive. The Bayesian adaptive approach to the
assessment of symptom-focused treatments may be an inportant avenue to address the critical need for an
evidence base for clinical decisions about symptom control. The significance of this research is that it addresses methods of reducing the symptoms and side effects of
aggressive cancer therapy. We will use new developments in clinical trial design that let us identify best
combined treatments for symptom reduction as quickly as possible, and let us administer the
effective treatment combinations to more patients within the period of the clinical trial. We aim to develop a
strategy using several drugs working together to prevent or ameliorate multiple treatment-related symptoms,
such as fatigue, pain, sleep disturbance, and poor appetite, for patients treated for head and neck cancer or
lung cancer. The ultimate goal of this research is to be able to administer the best available curative therapy
to the largest number of patients with the least treatment-related symptom burden.