Longitudinal Genome Wide Analysis of Patients with Chronic Lymphocytic Leukemia Reveals Complex Evolution of Clonal Architecture At Disease Progression and At the Time of Relapse

Abstract 2838 The combination of novel agents and chemo-immunotherapy (CIT) has led to a significant increase in both overall and complete response rates for chronic lymphocytic leukemia (CLL). There is, however, significant clinical heterogeneity in the overall effectiveness of CIT. Specifically, some patients are long-term responders who experience durable clinical remission while others, even though responding well initially, have less durable remissions. It is unclear if this heterogeneity is a reflection of an incomplete cytotoxic effect or whether certain leukemia sub-populations ( i.e. subclones) are resistant to the effects of CIT. Comprehensive analysis of tumor cells has provided, in some malignancies, evidence of extraordinary genomic complexity and intraclonal genetic diversity. Conversely, little is known of the genetic architecture of the leukemic clone in CLL patients and the extent to which CLL tumor cells differentiate genetically at the subclonal level. In order to gain insight into the genetic architecture and clonal evolution underlying disease progression/relapse, we performed a sequential, longitudinal whole genome analysis by array-based comparative genomic hybridization (aCGH) in 22 CLL cases with evidence of clinical progression and/or clinical relapse post therapy. These patients received standard purine analogue, cyclophosphamide and rituximab (PCR) for progressive disease. For the purposes of this study, we classified samples as; time point 1 (TP1) collected >6 months before starting first-line CIT for disease progression; time point 2 (TP2) consisting of tumor samples at the time of initial progression requiring treatment; and time point 3 (TP3) consisting of tumor samples collected >6 months after initial treatment but prior to subsequent treatments. Overall, aCGH assay was performed in 54 samples from 22 cases (2 to 4 time points analyzed per patient): 6 cases were analyzed at TP1 and TP2, 8 at TP2 and TP3, 2 at TP1 and TP3, and the remaining 6 cases were analyzed at TP1, TP2 and TP3. In cases showing genetic changes between time points, FISH analyses were performed in 32 additional samples to assess intra-clonal diversity and provide a quantification of the clone size. There was a small increase in genomic complexity in later time points with a mean of 4 (median 4, range 0–12) CNAs detected at the first time point for each patient, increasing to a mean of 5.3 (median 4.5, range 0–21) CNAs at the last time point analyzed. Diverse patterns of changes in genetic architecture were observed. The simplest change in genetic architectures was the linear sequence, found in 2 cases (9%) and characterized by the maintenance of the initial abnormalities and the subsequent acquisition of additional CNAs in the same subclone. The pattern of changes in genetic architecture in another 4 patients (18%) demonstrated marked heterogeneity including multiple genetic subclones evolving either in succession or in parallel. In all 4 cases with multiple subclones, a marked shift in the dominant subclone was observed at the time of disease relapse, with the minor subclones at TP1 and TP2 becoming the dominant subclones at TP3. In addition, the subclonal evolution continued following secondary therapies, and in all 4 cases the emerging subclone showed resistance to multiple treatments. This argues strongly in favor of subclones with specific genetic structures becoming dominant with the selective evolutionary pressure imposed by therapy. In summary, this sequential genetic study reveals that CLL leukemic B cells exhibit an unanticipated level of heterogeneity including a variable number of genetically distinct subclones, which display dynamic behavior in disease progression. The characterization of this intraclonal architecture is of critical importance to better understand the mechanisms of disease progression and relapse as well as to ultimately inform us of individualized treatment strategies. Disclosures: Fonseca: Consulting :Genzyme, Medtronic, BMS, Amgen, Otsuka, Celgene, Intellikine, Lilly Research Support: Cylene, Onyz, Celgene: Consultancy, Research Funding.