Dynamic Changes in Lymph Node Size and Skin Scoring Predict Response to Treatment in CTCL: An Interim Analysis
Authors: Fahmy Lauren M. , Yang Hye Ryung , Zhou Mengxi , Beylergil Volkan , Schreidah Celine M. , Bilgrami Zaid , Kwinta Bradley D. , Schwartz Lawrence , Fojo Antonio T. , Bates Susan E. , Geskin Larisa J. 
Published: December 2023

Cutaneous T-cell lymphoma (CTCL) is a heterogenous group of non-Hodgkin T-cell lymphomas with predominant skin involvement. Mycosis fungoides and Sézary syndrome are the most common subtypes and present with a wide spectrum of cutaneous and extra-cutaneous manifestations. Early-stage disease is often controlled with skin-directed therapies while advanced-stage disease can be treatment-refractory and often requires multiple lines of systemic therapy. It is therefore of great interest to identify early predictors of treatment response to tailor therapeutic management decisions.
Modified Severity-Weighted Assessment Tool (mSWAT) scoring and lymph node evaluation are important components of treatment response assessment in clinical practice and in trials. A major limitation of current assessment methods is the use of relative changes in mSWAT and lymph node size which do not account for heterogeneous treatment effects. Novel assessment techniques that reflect the dynamic changes occurring within the tumor environment during treatment have the potential to offer more accurate insights into treatment response than current methods. Among these novel approaches is kinetic modeling of tumor growth rate ( g), which has been validated as a biomarker for treatment response and survival in other types of cancer but has never been used in CTCL. Kinetic modeling provides the benefit of evaluating the simultaneous rates of growth of treatment-resistant cells and regression of treatment-responsive cells. We hypothesized that dynamic changes in lymph node size and mSWAT scores over time could predict response to treatment in CTCL patients.
We retrospectively analyzed clinical data and computed tomography scans from 119 patients enrolled in the MAVORIC trial. An open-source imaging platform with integrated semi-automated segmentation algorithms was used to quantify lymph node volumes for each patient. Kinetic modeling was applied to estimate the simultaneous rates of growth ( g) and regression ( d) of lymph node size and mSWAT scores during treatment. Patients were divided into two groups based on g and correlation with time to treatment failure (TTF) was evaluated. Tumor doubling times were estimated using the equation dt = 0.693/ g.
For most patients, g and d could be estimated, and these values could be determined early in the treatment course. Volumetric lymph node growth rate had an inverse correlation with TTF (p &lt; 0.0001). Patients with with faster rates of nodal growth (median g = 0.0077 d -1, doubling time = 90 days) had a median TTF of 2.9 months, whereas patients with slower rates of nodal growth (median g = 0.0013 d -1, doubling time = 533 days) had a median TTF of 8.8 months. Rate of growth of mSWAT scores also had an inverse correlation with TTF (p &lt; 0.0001). Those with the fastest rate of increase in mSWAT scores (median g = 0.0057 d -1, doubling time = 122 days) had a median TTF of 2.9 months while those with the slowest rate of increase (median g = 0.0013 d -1, doubling time = 533 days) had a median TTF of 8.5 months.
In this interim analysis, we use a novel kinetic model to demonstrate that g correlates with time to treatment failure, suggesting that lymph node and mSWAT growth kinetics may be early predictors of treatment response in CTCL. Our findings have potential implications for both clinical practice and trials. Lymph node and mSWAT growth rates may support clinicians in making early decisions about adapting treatment regimens for individual patients. In clinical trials and early drug development, the incorporation of g as an endpoint may accelerate the recognition of promising drug candidates. Our findings require validation in a larger cohort as well as prospective studies.