Computer-based assessment of tumor microenvironment (TME) in Follicular Lymphoma

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DESCRIPTION (provided by applicant): The overall goals of this proposal are to: 1) Measure the prognostic impact of histologic grade (without and with computer assistance) of follicular lymphoma cases by comparing it with outcome measures; and 2) to develop a computer-assisted image analysis (CaIA) system to quantitatively assess the FL tumor microenvironment (TME); 3) Compare the effectiveness of combined prognostic measure incorporating grade (without and with computer assistance), TME parameters and existing FLIPI score. The proposed research aims to develop a clinically relevant, pathology-based prognostic model in FL utilizing computer image analysis to incorporate grade, tumor microenvironment (TME), and immunohistochemical (IHC) markers. Due to the variable clinical course in FL and increasing treatment options, a prognostic index would allow therapies to be tailored to the patient. Patients with high risk disease may benefit form more intensive therapy, while patients with low risk disease may be appropriate for lower intensity therapy with a more favorable side effect profile. Furthermore, a prognostic index, which includes pathologic features, may ultimately become more relevant in the era of biologically targeted therapies. Our objective is to use advanced image analysis techniques to perform a quantitative and topographical study of the normal and tumor microenvironment and use this study as well as improved and consistent grading options in improving the current prognostic index. Our long-term goal is to translate the improved prognostic index results as better treatment options to FL patients. We plan to pursue the following three specific aims for this project: Specific Aim 1: Measure the prognostic impact of histologic grade (without and with computer assistance) of follicular lymphoma cases by comparing it with outcome measures; Specific Aim 2: Measure the impact of FL tumor microenvironment by comparing TME parameters with outcome measures; Specific Aim 3: Compare the effectiveness of combined prognostic measure incorporating grade (without and with computer assistance), TME parameters and existing FLIPI score. We have formed an experienced team with expertise in FL pathology and oncology, imaging and image analysis, observer studies and biostatistics. Successful completion of this project will exert a sustained and powerful impact on the field by the virtue of its development of a platform for researchers and clinicians to quantitatively and objectively evaluate FL TME, to improve the FL grading, and to incorporate these developments to form a better prognostic index. Microscopic image analysis software, which will be developed for quantification, will be usable for other diseases such as breast cancer, for which TME is also known to be an important predictor of clinical status. The software and data to carry out this project will be made freely available to the research community.