A method for assessing tumor response to therapy and more precisely guiding treatment decisions so as to improve survival.
e13122 Background: The response of tumors to chemotherapy is monitored using imaging data or tumor markers and this quantitative data provides a rich source for an objective response assessment and treatment decisions. Responses are usually assessed as categorical variables based on percentage increase or decrease in tumor size. Methods: We have developed mathematical equations that describe efficacy as a continuous variable, enabling the extraction of the appropriate rate constants for tumor growth and regression (decay), designated g and d, respectively. Both are used to describe the rates of tumor growth and regression for the fraction of tumor that is growing despite treatment and the fraction dying as a result of therapy, respectively. Results: Using data from randomized phase III trials in kidney and breast cancer, multiple myeloma, and medullary thyroid carcinoma; as well as phase II trials in prostate cancer we have shown that: (1) values of g but not those of d are strongly correlated (negatively) with patient survival; (2) g can be discerned early in treatment, before growth is demonstrated clinically, providing an early efficacy measure; (3) g typically does not change over time, even over years, suggesting resistance is intrinsic and predictable and does not worsen over time; (4) effective therapies both increase d, and reduce g; and (5) in every cancer studied, the evidence suggests tumor growth reverts to its pre-treatment rate when chemotherapy is discontinued. Conclusions: The observation that g remains stable allows one to predict the most likely outcome of continued therapy. The evidence indicates that the increase in g occurring after treatment discontinuation is due to a resumption of a pre-treatment growth rate and not a change in biology. Our hypothesis is that if a favorable growth rate that slows tumor growth can be identified, survival might be improved if therapies that achieve this favorable growth rate are continued despite crossing conventional disease progression boundaries. We plan a prospective test of this model to provide a more informed decision and better survival outcome by maximizing the benefit obtained from approved therapies.