GCH-CLL

a. Background and rationale: The clinical course and eventual outcome of Chronic Lymphocytic Leukemia (CLL) patients is highly variable. It is accepted that intratumor genetic and cellular heterogeneity is the main force driving both tumor development and treatment resistance. At the genetic level, there is no single gene mutation characteristic of the disease and CLL cells in a given patient may carry different burden of different mutations affecting different pathways. At the cellular level, leukemic cells remain responsive to multiple stimuli originating from microenvironment but with different intensity and effect. Available biomarkers aiming at predicting both the disease course and the response to treatment exist, but none of the current models capture the complexity of intratumor heterogeneity.
b. Hypothesis: Intratumor genetic and cellular heterogeneity of CLL has tremendous implications for treatment and outcome, but is currently poorly understood. Novel means of observing and quantifying this heterogeneity together with integrative and predictive mathematical modeling will help identifying strategies to prevent treatment resistance.
c. Aims:
- To comprehensively characterize the biological and cellular heterogeneity of CLL, at the intratumor level, and evaluate its impact on disease evolution and response to therapy. Samples from different involved tissues and at different time points during CLL evolution, including stable, progressive, and relapsed after treatment will be used.
- To monitor cellular response to drug exposure in vitro in a large-scale study of genetically characterized primary CLL cells, from different tissues, with and without microenvironmental stimuli, to obtain robust data on the impact of mutations load and other biomarkers on drug response.
- To integrate data of different types and scales with clinical information, including response to treatment, using up to date bioinformatics and mathematical modeling approaches.
d. Methods: Cellular and molecular biology methods, advanced data analytics and mathematical modeling.
e. Expected results and potential impact: We propose a concerted effort combining genomic technologies, cellular biology approaches, statistical analysis and mathematical modeling that will build a comprehensive framework to establish the role of intratumor heterogeneity in the progression of the tumor, modulation of the response to the treatment and development of resistance. The integrated analysis proposed here will allow the development of robust predictive tools for monitoring CLL from diagnosis to the different steps in the evolution of the disease. This project is ideally timed and positioned to exploit a paradigmatic age-related malignancy with marked intratumor heterogeneity, heterogeneous clinical course and response to treatment.