Genetic landscape of ultra-stable chronic lymphocytic leukemia patients

Abstract

Background: Chronic lymphocytic leukemia (CLL) has a heterogeneous clinical course. Beside patients requiring immediate treatment, others show an initial indolent phase followed by progression and others do not progress for decades. The latter two subgroups usually display mutated IGHV genes and a favorable FISH profile.

Patients and methods: Patients with absence of disease progression for over 10 years (10-34) from diagnosis were defined as ultra-stable CLL (US-CLL). Forty US-CLL underwent extensive characterization including whole exome sequencing (WES), ultra-deep sequencing and copy number aberration (CNA) analysis to define their unexplored genetic landscape. Microarray analysis, comparing US-CLL with non-US-CLL with similar immunogenetic features (mutated IGHV/favorable FISH), was also carried out to recognize US-CLL at diagnosis.

Results: WES was carried out in 20 US-CLL and 84 non-silent somatic mutations in 78 genes were found. When re-tested in a validation cohort of 20 further US-CLL, no recurrent lesion was identified. No clonal mutations of NOTCH1, BIRC3, SF3B1 and TP53 were found, including ATM and other potential progression driving mutations. CNA analysis identified 31 lesions, none with known poor prognostic impact. No novel recurrent lesion was identified: most cases showed no lesions (38%) or an isolated del(13q) (31%). The expression of 6 genes, selected from a gene expression profile analysis by microarray and quantified by droplet digital PCR on a cohort of 79 CLL (58 US-CLL and 21 non-US-CLL), allowed to build a decision-tree capable of recognizing at diagnosis US-CLL patients.

Conclusions: The genetic landscape of US-CLL is characterized by the absence of known unfavorable driver mutations/CNA and of novel recurrent genetic lesions. Among CLL patients with favorable immunogenetics, a decision-tree based on the expression of 6 genes may identify at diagnosis patients who are likely to maintain an indolent disease for decades.

Figure 1.

(A) WES in the discovery cohort. Number of non-silent somatic mutations per case. The 10 genes recurrent in ≥2 cases are reported. (B) CNA analysis. Number of CNA per case and per chromosome.

Figure 2.

(A) Gene expression profile analysis. The figure shows the 32 genes that most significantly identify US-CLL patients (see supplementary Figure S1, available at Annals of Oncology online for more details on microarray work-flow) at the supervised analysis. On the right, the 10 candidate classifier genes. (B) Decision-tree. The decision-tree is derived from the best predictive model in the R output, identifying eight subgroups (nodes) and six associated factors. The final decision-tree had the first split at P2RX1 expression value of 0.0053, the second decision node at PLXND1 expression value of 0.57, the third at CPT1A expression value of 0.21. In the fourth split for PRRCR2 expression values between 1.7 and 2.4 the patient was classified as US-CLL, for values <1.7 as non-US-CLL, for values ≥2.4 the evaluation of the next genes was required. The expression values derive from ddPCR quantification and represent an absolute measure of copies of each target gene/μl of reaction. ^§One non-US-CLL misclassified as US-CLL. *Two US-CLL misclassified as non-US-CLL. **One US-CLL misclassified as non-US-CLL.