Tailored approaches grounded on immunogenetic features for refined prognostication in chronic lymphocytic leukemia

Abstract

Chronic lymphocytic leukemia (CLL) patients with differential somatic hypermutation status of the immunoglobulin heavy variable genes, namely mutated or unmutated, display fundamental clinico-biological differences. Considering this, we assessed prognosis separately within mutated (M-CLL) and unmutated (U-CLL) CLL in 3015 patients, hypothesizing that the relative significance of relevant indicators may differ between these two categories. Within Binet A M-CLL patients, besides TP53 abnormalities, trisomy 12 and stereotyped subset #2 membership were equivalently associated with the shortest time-to-first-treatment and a treatment probability at five and ten years after diagnosis of 40% and 55%, respectively; the remaining cases exhibited 5-year and 10-year treatment probability of 12% and 25%, respectively. Within Binet A U-CLL patients, besides TP53 abnormalities, del(11q) and/or SF3B1 mutations were associated with the shortest time-to-first-treatment (5- and 10-year treatment probability: 78% and 98%, respectively); in the remaining cases, males had a significantly worse prognosis than females. In conclusion, the relative weight of indicators that can accurately risk stratify early-stage CLL patients differs depending on the somatic hypermutation status of the immunoglobulin heavy variable genes of each patient. This finding highlights the fact that compartmentalized approaches based on immunogenetic features are necessary to refine and tailor prognostication in CLL.

Figure 1.

Kaplan-Meier curves for time-to-first-treatment (TTFT). (A) In Binet A, B and C mutated chronic lymphocytic leukemia (M-CLL) patients and (B) Binet A, B and C unmutated chronic lymphocytic leukemia (U-CLL) patients.

Figure 2.

Subgroups of patients with similar prognosis within mutated chronic lymphocytic leukemia (M-CLL) and unmutated chronic lymphocytic leukemia (U-CLL). (A) TP53abn, trisomy 12 (+12) and stereotyped subset #2 assignment define three almost non-overlapping groups within early stage M-CLL. (B) Binet A M-CLL cases with TP53abn, +12 or assignment to stereotyped subset #2 display similar time-to-first-treatment (TTFT.) (C) Distribution of TP53abn, SF3B1 mutations and del(11q) in Binet A U-CLL. (D) Binet A U-CLL cases carrying TP53abn, SF3B1 mutations or del(11q) exhibit similar TTFT. N: number of patients.

Figure 3.

Application of binary recursive partitioning in mutated chronic lymphocytic leukemia (M-CLL) and unmutated chronic lymphocytic leukemia (U-CLL). (A) Decision tree for Binet A M-CLL based on binary recursive partitioning and the subsequent application of an amalgamation algorithm. Trisomy 12 (+12), TP53abn and subset #2 membership were found to be the most significant factors as determined by the partitioning algorithm. The Binet A population is split in 4 terminal nodes (4, 5, 6 and 7). The amalgamation algorithm applied subsequently merged 3 of them in a larger terminal node. In particular, +12 was considered as the covariate with the strongest association to time-to-first-treatment (TTFT). Amongst patients lacking +12, TP53abn was the co-variate with the strongest association to TTFT and so on. After applying the amalgamation algorithm, patients with +12 and/or TP53abn and/or assignment to subset #2 were grouped into a larger node, resulting in 2 terminal nodes. The splitting is performed from right to left, following the criterion of strongest factor association with TTFT. The right branch represents the presence of a particular factor and the left branch the absence of that factor. P-value corresponds to a log-rank scores based test. The Kaplan-Meier curves estimate the TTFT of patients within each terminal node and n represents the number of patients per node. (B) Decision tree for Binet A U-CLL based on binary recursive partitioning and the subsequent application of an amalgamation algorithm. Male sex, TP53abn and del(11q) were the most significant factors as determined by the partitioning algorithm. The Binet A population was split into 6 terminal nodes (4, 5, 6, 9, 10 and 11). The amalgamation algorithm applied merged 3 of the terminal nodes into a larger terminal node. Sex was deemed to be the co-variate with the strongest association to TTFT. Amongst male patients, TP53abn was the co-variate with strongest association to TTFT. Amongst female patients, del(11q) was the co-variate with strongest association to TTFT and so on. After applying the amalgamation algorithm, male patients without TP53abn and with del(11q), and female patients with del(11q) and/or TP53abn were grouped into a larger node. The final number of terminal nodes was 4. The splitting is performed from top to bottom, following the criterion of strongest factor association with TTFT. The right branch represents the presence of a particular factor and the left one the absence of that factor. P-value corresponds to a log-rank scores-based test. The Kaplan-Meier curves estimate the TTFT of patients within each terminal node and n represents the number of patients per node.

Figure 4.

Prognostic index for time-to-first-treatment (TTFT) for mutated chronic lymphocytic leukemia (M-CLL) and unmutated chronic lymphocytic leukemia (UCLL). (A) Prognostic index for TTFT within M-CLL: i) very high risk: Binet C; ii) high risk: Binet B; iii) intermediate risk: Binet A with at least one of the following: TP53abn or +12 or assignment to subset #2 and; iv) low risk: non-TP53abn/+12/#2 Binet A. (B) Prognostic index for TTFT within U-CLL: i) very high risk: Binet C; ii) high risk: Binet B; iii) intermediate risk: Binet A with at least one of the following: TP53abn or +SF3B1 mutations or del(11q) membership; iv) low risk: non-TP53abn/SF3B1mut/del(11q) male Binet A and; v) very low risk: non-TP53abn/SF3B1mut/del(11q) female Binet A.

Figure 5.

Kaplan-Meier curves for time-to-first-treatment (TTFT) in the validation cohort. (A) Within mutated chronic lymphocytic leukemia (M-CLL), Binet A cases positive for TP53abn, trisomy 12 (+12) and stereotyped subset #2 assignment display similar TTFT. (B) No difference regarding TTFT among Binet A and unmutated chronic lymphocytic leukemia (U-CLL) cases carrying TP53abn, SF3B1 mutations or del(11q) in the validation cohort.

Figure 6.

Kaplan-Meier curves for time-to-first-treatment (TTFT) for mutated chronic lymphocytic leukemia (M-CLL) and unmutated chronic lymphocytic leukemia (U-CLL) cases carrying trisomy 12 (+12). (A) +12 is an unfavorable prognosticator in M-CLL. (B) +12 is associated with a more indolent clinical course in U-CLL.

Figure 7.

Prognostic algorithm regarding treatment probability for chronic lymphocytic leukemia (CLL). 5y-TP: treatment probability five years from diagnosis; 10y-TP: treatment probability ten years from diagnosis; U-CLL: CLL with unmutated IGHV genes; M-CLL: CLL with mutated IGHV genes; TP53abn: deletion of chromosome 17p (del(17p)) and/or TP53 mutation; +12:trisomy 12, del(11q): deletion of chromosome 11q; SF3B1mut: SF3B1 mutation; #2: assignment to stereotyped subset #2. The pie chart refers to the entire cohort with each slice indicating the proportion of patients according to Binet clinical staging.