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. 2022 Nov 8;6(21):5763-5773.
doi: 10.1182/bloodadvances.2022007508.

A gene expression assay based on chronic lymphocytic leukemia activation in the microenvironment to predict progression

Affiliations

A gene expression assay based on chronic lymphocytic leukemia activation in the microenvironment to predict progression

Pau Abrisqueta et al. Blood Adv. .

Abstract

Several gene expression profiles with a strong correlation with patient outcomes have been previously described in chronic lymphocytic leukemia (CLL), although their applicability as biomarkers in clinical practice has been particularly limited. Here we describe the training and validation of a gene expression signature for predicting early progression in patients with CLL based on the analysis of 200 genes related to microenvironment signaling on the NanoString platform. In the training cohort (n = 154), the CLL15 assay containing a 15-gene signature was associated with the time to first treatment (TtFT) (hazard ratio [HR], 2.83; 95% CI, 2.17-3.68; P < .001). The prognostic value of the CLL15 score (HR, 1.71; 95% CI, 1.15-2.52; P = .007) was further confirmed in an external independent validation cohort (n = 112). Notably, the CLL15 score improved the prognostic capacity over IGHV mutational status and the International Prognostic Score for asymptomatic early-stage (IPS-E) CLL. In multivariate analysis, the CLL15 score (HR, 1.83; 95% CI, 1.32-2.56; P < .001) and the IPS-E CLL (HR, 2.23; 95% CI, 1.59-3.12; P < .001) were independently associated with TtFT. The newly developed and validated CLL15 assay successfully translated previous gene signatures such as the microenvironment signaling into a new gene expression-based assay with prognostic implications in CLL.

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Conflict of interest statement

Conflict-of-interest disclosure: P.A. received honoraria from Janssen, Roche, Celgene, AbbVie, and AstraZeneca. G.V. received research honoraria for speaker activities from MSD and an advisory role from AstraZeneca. M.A. received honoraria for speaker activities from AstraZeneca, an advisory role from Janssen, and nonfinancial support from Janssen, and AbbVie. A.M.-N. received honoraria from Janssen, Roche, Takeda, Gilead, AbbVie, and Celgene for speaker activities and from Janssen, Takeda, Gilead, Kiowa Kirin, AstraZeneca, and Beigene for participating in advisory boards. M.C. received research funding from Janssen, Roche, and AstraZeneca. F.B. received honoraria and research grants from Roche, Celgene, Takeda, AstraZeneca, Novartis, AbbVie, Lilly, Beigene, and Janssen. The remaining authors declare no competing financial interests.

Figures

Figure 1
Figure 1.
The geneexpression–based model to predict TtFT in patients with CLL. A heatmap of the CLL15 assay with 15 informative genes shown as rows and 154 patient samples as columns. The 3 patient groups identified by the assay are shown below the heatmap together with the mutational status of the IGHV genes.
Figure 2
Figure 2.
Time to first treatment in the training cohort. (A) Log-relative hazard according to the CLL15 score. (B) Kaplan-Meier curves of the TtFT of the 3 patient groups identified by the CLL15 assay. (C) Kaplan-Meier curves of the TtFT of the 3 patient groups identified by the CLL15 assay in the subgroup of patients with early-stage disease (Binet A 0/I). (D) Kaplan-Meier curves of the TtFT according to CLL15 assay and IGHV mutational status.
Figure 3
Figure 3.
Univariate and multivariate analysis for TtFT according to prognostic factors in CLL.
Figure 4
Figure 4.
Different models to predict TtFT according to CLL15 score, IGHV and IPS-E CLL score. (A) Discrimination capacity in terms of C-statistic according to models including CLL15 score, IGHV mutational status, and IPS-E CLL score. (B) Pairwise ANOVA comparisons. ANOVA, analysis of variance.
Figure 5
Figure 5.
Time to first treatment in the validation cohort. (A) Log-relative hazard according to the CLL15 score in the validation cohort. (B) Kaplan-Meier curves of the TtFT of the 3 patient groups in the validation cohort identified by the CLL15 assay.

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