According to researchers, the dynamics of CLL growth observed prior to treatment provide insight into the trajectory of the disease. The findings from this study were published in Nature.
While genomic characterizations of cancers have become increasingly detailed within the past 5 years, the impact of tumor genomic features on the phenotypic characteristics of cancers has not been well explored.
Characteristics of CLL that make it a good model to study cancer phenotype include its indolent course in many patients, allowing for the postponement of treatment for months to years. However, CLL is also characterized as having a highly variable clinical course from one individual to another, thereby providing a means of comparing the tumor molecular landscape with clinical outcomes and the natural history of cancer growth.
In this study, 21 patients diagnosed with CLL were identified who had a median of 22 pretreatment serial venipuncture samples and a time period between diagnosis and initiation of treatment ranging from 2.1 years to 15.6 years (ie, all patients received treatment).
The CLL growth patterns were estimated using peripheral white blood cell counts, and categorized as logistic (ie, bounded; a plateauing of white blood cells over time), exponential (ie, unbounded), or indeterminate (ie, due to short observation period or complex growth pattern).
Whole-exome sequencing was performed on a median of 3 pretreatment samples per patient. Findings for this discovery cohort were evaluated in the context of an independent validation cohort of 85 patients with CLL in which a median of 18 serial samples were collected.
Of the 21 patients in the discovery cohort, cancer growth patterns were classified as logistic, exponential, and indeterminate in 5, 10, and 6 patients, respectively. At least 1 known CLL driver mutation was detected in all 21 patients, although the median number of CLL driver mutations was 1, 2, and 4 in patients with disease characterized by logistic, exponential, and indeterminate growth, respectively (P =.005 for comparison of the former with the latter 2 categories).
In addition, a trend toward an increased frequency of IGHV mutations — a previously identified biomarker of good prognosis — was observed for patients with logistic versus exponential cancer growth (P =.12). These results were confirmed in the validation cohort, where trisomy 12 and the cytogenetic marker, del(13q), were found to be more common in cancers characterized by exponential and logistic growth patterns, respectively.
Importantly, on multivariate analysis, CLL growth pattern was shown to “contribute additional explanatory power” beyond that obtained using del(13q) and the last white blood cell count.
Interestingly, a higher degree of clonal evolution in clones characterized by CLL driver mutations was observed in patients exhibiting exponential CLL growth compared with patients with disease characterized as logistic or indeterminate (P =.033) in samples taken from patients not yet exposed to CLL therapy. This association was also observed in the validation cohort. Furthermore, in the validation cohort where not all patients received treatment, the treatment rate was highest in those with disease characterized by exponential growth compared with patients with indeterminate or logistic CLL growth patterns (ie, 75%, 67%, and 21%, P <.001).
Of the 21 patients in the discovery cohort, 12 patients experienced a disease relapse following treatment, and posttreatment samples were available for 10 of these patients. A pretreatment exponential growth pattern was observed in all patients with clonal evolution of CLL following therapy. Furthermore, clonal architecture was preserved following treatment in all patients with a pretreatment logistic or indeterminate CLL growth pattern.
In providing context for these results, the study authors noted that “in a subset of patients, whose serial samples underwent next-generation sequencing, we found that dynamic changes in the disease course of CLL were shaped by the genetic events that were already present in the early slow-growing stages.”
Another interesting observation made in this study was related to an investigation of the growth dynamics of populations of subclones in these samples compared with their parental clones. One finding was that subclones acquiring additional CLL drivers had a fitness advantage in that they were more likely than the latter to exhibit accelerated growth, thereby demonstrating “ongoing evolution in vivo.”
“These fundamental findings are especially relevant to the ongoing efforts of precision oncology, in which the estimation of clonal growth dynamics in individual patients may inform therapy and predict the course of their disease,” the study authors concluded.