Genetic and cellular intratumor heterogeneity as predictor of chronic lymphocytic leukemia outcome and treatment resistance
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.
Chronic lymphocytic leukemia (CLL) is the most frequent adult leukemia in the west. The clinical course and eventual outcome of CLL is remarkably heterogeneous.
As a consortium, we have sequenced >1000 CLL samples with more than 28 genes or genomic regions. We showed the importance of quantifying the percentage of cells carrying each specific mutation and reconstructed their evolutionary trajectories.
This shows 1) copy number alterations (CNA) tend to be acquired early whereas gene mutations usually occur later during CLL progression. 2) Total number of driver alterations is associated with shortened the time to first treatment. 3) Increasing subclonal heterogeneity (accumulation of driver alterations in subclones) was best at predicting overall survival of the patients. 4) The mutation profiles are very similar between monoclonal B-cell lymphocytosis and ultra-stable chronic lymphocytic leukemia 5) No topographic differences were observed for mutations in known driver genes such as NOTCH1 , TP53 , ATM , SF3B1 , IRF4 and DDX3X . 5) We reported a novel U1 mutation which identifies a subgroup of patients with aggressive disease 6) Complex karyotype CLL represents a heterogeneous group with variable clinical behavior.
We also investigated the functional relationship between mutation, drugs and microenvironment. We developed a novel software, called multi-omics factor analysis (MOFA), which identify common underlying factors (latent variables) shared between the drug response data and the ‘omics datatypes. In vitro analyses of drug response showed that somatic mutations influence drug responses in CLL.
We showed that the heterogeneity in energy metabolism may be therapeutically exploited in the selection of therapeutic strategies. Experimentally, microenvironmental stimuli affect EZH2 expression and function in CLL and combined B-cell signaling and EZH2 inhibition showed synergistic effects on primary CLL cells. We also uncovered that in vivo ibrutinib treatment reprograms CLL cell signaling capacity in a heterogeneous manner.
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This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 964264.