REGULAR ARTICLE A gene expression assay based on chronic lymphocytic leukemia activation in the microenvironment to predict progression Pau Abrisqueta,1,2 Daniel Medina,2,3 Guillermo Villacampa,4 Junyan Lu,5 Miguel Alcoceba,6 Julia Carabia,2,3 Joan Boix,2,3 Barbara Tazón-Vega,1,2 Gloria Iacoboni,1,2 Sabela Bobillo,1,2 Ana Marín-Niebla,1,2 Marcos González,6 Thorsten Zenz,7, * Marta Crespo,2,3, * and Francesc Bosch1,2, * 1 Servei d’Hematologia, Vall d’Hebron Hospital Universitari, Experimental Hematology, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain; 2 Departament de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain; 3 Experimental Hematology, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain; 4 Oncology Data Science (OdysSey) Group, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain; 5 European Molecular Biology Laboratory (EMBL), Heidelberg, Germany; 6 Department of Hematology, University Hospital of Salamanca (HUS/IBSAL), CIBERONC and Center for Cancer Research-IBMCC (USAL-CSIC), Salamanca, Spain; and 7 Department of Medical Oncology and Hematology, University Hospital and University of Zurich, Zurich, Switzerland Several gene expression profiles with a strong correlation with patient outcomes have been Key Points previously described in chronic lymphocytic leukemia (CLL), although their applicability as • The CLL15 signature, biomarkers in clinical practice has been particularly limited. Here we describe the training based on the and validation of a gene expression signature for predicting early progression in patients expression of genes with CLL based on the analysis of 200 genes related to microenvironment signaling on the associated with NanoString platform. In the training cohort (n = 154), the CLL15 assay containing a 15-gene microenvironment signature was associated with the time to first treatment (TtFT) (hazard ratio [HR], 2.83; 95% signaling, predicts early progression in CLL. 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). • The predictive power Notably, the CLL15 score improved the prognostic capacity over IGHV mutational status of the CLL15 signature and the International Prognostic Score for asymptomatic early-stage (IPS-E) CLL. In is independent of the multivariate analysis, the CLL15 score (HR, 1.83; 95% CI, 1.32-2.56; P < .001) and the IPS-E IGHV mutational status and IPS-E CLL score. 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. Introduction It is well accepted that patients with chronic lymphocytic leukemia (CLL) who are asymptomatic and in an early clinical phase do not require therapy.1 Nevertheless, cumulative data on the risk of clonal evolution2-4 renewed interest in early therapeutic intervention in patients at diagnosis who are likely to progress rapidly.5 Therefore, the identification of these patients at diagnosis has been an intense focus of clinical research in the field of CLL. Prognostication in this setting has classically relied on a myriad of laboratory values, cytogenetic abnormalities, gene mutations, or the mutational status of the IGHV genes.6-10 More recently, the International Prognostic Score for Early-stage CLL (IPS-E) has been Submitted 7 March 2022; accepted 28 July 2022; prepublished online on The full-text version of this article contains a data supplement. Blood Advances First Edition 16 August 2022. https://doi.org/10.1182/ © 2022 by The American Society of Hematology. Licensed under Creative Commons bloodadvances.2022007508. Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), *T.Z., M.C., and F.B. contributed equally to this study. permitting only noncommercial, nonderivative use with attribution. All other rights Data are available on request from the corresponding author, Pau Abrisqueta reserved. (pabrisqueta@vhio.net). 8 NOVEMBER 2022 • VOLUME 6, NUMBER 21 5763 developed employing 3 covariates: unmutated IGHV, absolute Technologies), and the final tumor content was assessed by flow lymphocyte count > 15 × 109/L, and presence of palpable lymph cytometry. The estimated median tumor content was 98.3% nodes.11 (range, 80-99.9) in the training cohort and 95.7% (range, 86.8%- 99.4%) in the validation cohort. Despite this extensive investigation, the accuracy of these models may be improved.11-16 In addition, the emergence of novel targeted Written informed consent was obtained from all individuals in agents has attracted interest in the early treatment of patients at accordance with the Declaration of Helsinki. The study was high risk of early progression.5 approved by the clinical research ethics committee of the Vall d’Hebron Barcelona Hospital Campus. Gene expression profiles and the clinical course of patients with CLL have been correlated in various studies.7,8,17-26 Unfortunately, bio- Gene expression analysis markers based on gene expression profiles exhibit several caveats that preclude them from being widely applied in the prognostication Gene expression was quantified in 250 ng of RNA on the Nano- of patients with CLL. These include the lack of reproducibility and String platform (NanoString Technologies, WA) using the “high standardization and the complexity of bioinformatics analysis. sensitivity” setting on the nCounter PrepStation and 555 fields of Significantly, the prognostic value of clustering methods is limited by view on the nCounter Digital Analyzer. A total of 178 genes were the fact that the assignment of an individual may vary when different selected from the literature, including genes related to the activa- patients are included in the clustering process, thus impeding the tion of CLL cells in the microenvironment,23-26 genes that were use of these methods in real time. In this regard, the development of differentially expressed according to the mutational status of new platforms that allow direct and reproducible quantification of IGHV,7,8,17,18 and other genes of prognostic interest in CLL gene expression, such as NanoString nCounter, should facilitate the (supplemental Methods, supplemental Table 1). Normalization for attainment of gene expression biomarkers applicable in clinical RNA loading was performed using the geometric mean of 22 settings.27,28 Among different gene signatures, and because CLL is housekeeping genes (supplemental Table 1). The normalized data a malignancy that is particularly dependent on interaction with the were log10 transformed. The reference gene selection is further microenvironment for survival and proliferation,25 IGHV mutational described in the “Data supplement.” status signature7,8,17,18 and genes involved in the activation of Predictive gene expression score malignant cells in the microenvironment, including stimulation of the B-cell receptor (BCR),24-26,29 are of particular interest. Indeed, this Detailed descriptions of model building and performance assess- notion is reinforced by the standard use of different small molecules ment are provided in the “Data supplement.” In brief, we used the targeting CLL-microenvironment interactions, particularly Bruton’s gene expression data from the training cohort to produce a parsi- tyrosine kinase inhibitors.30,31 monious predictive model for time to first treatment (TtFT) using a penalized Cox model.33 To evaluate the global performance of the Herein, we developed, evaluated, and validated a multigene multivariate Cox model obtained from the selected genes, different expression signature using genes associated with the activation of diagnostic parameters were calculated and are summarized in the CLL cells in the microenvironment and the IGHV mutational status. “Data supplement” (supplemental Table 2), including R2, Brier This assay, based on the NanoString platform, should facilitate its score, iAUC (a summary measure of the area under the receiver applicability in clinical settings. operating characteristic curve calculated for the different times), and Harrell’s C-statistic, a generalization of the AUC.34,35 The graph obtained for the AUC values at the different time points is Materials and methods shown in supplemental Figure 2. For illustrative purposes, we Study design and patient population dichotomized the predictive gene expression score in 3 risk groups using the R partykit package. The overall design of the process for developing and evaluating a new assay to assess the risk of progression in patients with CLL is Statistical analysis shown in supplemental Figure 1. For the training cohort of the The statistical analysis plan was prespecified before the evaluation study, 156 untreated samples, 119 from the University Hospital Vall of the gene expression in the training and validation cohort. The d’Hebron and 37 from the University of Salamanca, were used. The primary end point of the study was TtFT, defined as the time from assay was validated using 112 samples from an independent the date of obtaining the sample to the date of treatment onset. To cohort of patients from the German Cancer Research Center, study the predictive capacity of the gene expression score, we Heidelberg, Germany. The details of the validation cohort have relaxed the linearity assumption using restricted cubic splines by been reported elsewhere.32 means of rms R package (Harrell, F. E. Jr Package “rms” [The Samples were obtained at diagnosis, whenever possible. For Comprehensive R Archive Network, 2016]). Harrell’s C-statistic patients who did not have a sample at the time of diagnosis, was calculated to compare the discrimination capacities of samples were collected during follow-up but always before the different models. The analysis of deviance (analysis of variance R patients received any treatment. Gene expression quantification function) was used to study whether the inclusion of new factors was performed in blood samples from untreated patients diag- had a significant improvement in the predictive capacity of the nosed with CLL. Peripheral blood mononuclear cells were obtained model. Survival curves were estimated using the Kaplan-Meier using Ficoll-Paque Plus density gradient (GE Healthcare, Buck- method to visualize gene expression risk groups and were inghamshire, United Kingdom) and subsequently cryopreserved compared by the log-rank test. Cox proportional-hazard models until analysis. Tumor cells were purified using immunomagnetic were used to obtain hazard ratios (HRs) with 95% CIs without depletion by EasySep Human B Cell Enrichment Kit (StemCell dichotomizing continuous factors.36 To select prognostic variables 5764 ABRISQUETA et al 8 NOVEMBER 2022 • VOLUME 6, NUMBER 21 Table 1. Patient and disease characteristics of the training cohort CLL15 categories Total cohort Low-risk group Intermediate-risk group High-risk group (n = 154), n (%) (n = 85), n (%) (n = 31), n (%) (n = 38), n (%) P value Male 88 (57.1) 47 (55.3) 16 (51.6) 25 (65.8) .435 Female 66 (42.9) 38 (44.7) 15 (48.4) 13 (34.2) Age, median (range) years 70 (34-91) 72 (34-91) 69 (46-91) 64 (44-85) .05 Binet stage <.01 A 116 (76.3) 79 (92.9) 24 (80) 13 (35.1) B 27 (17.8) 6 (7.1) 4 (13.3) 17 (45.9) C 9 (5.9) 0 2 (6.7) 7 (18.9) Missing, n 2 — 1 1 Lymphocyte cell count, 10 /L – median (range) 9 16.8 (3.2-323) 16.8 (3.2-238) 15.6 (4.2-323) 22.1 (7.9-207.4) .18 Missing, n 38 3 7 28 β2-microglobulin <.01 ≤3.5 mg/dL 108 (74) 68 (81.9) 24 (85.7) 16 (45.7) >3.5 mg/dL 38 (26) 15 (18.1) 4 (14.3) 19 (54.3) Missing, n 8 2 3 3 CLL-IPI <.01 Low (0-1) 54 (43.9) 38 (56.7) 11 (42.3) 5 (16.7) Intermediate (2-3) 32 (20.8) 16 (23.9) 9 (34.6) 7 (23.3) High (4-6) 31 (20.1) 12 (17.9) 5 (19.2) 14 (46.7) Very high (7-10) 6 (3.9) 1 (1.5) 1 (3.8) 4 (13.3) Missing, n 31 18 5 8 CLL IPS-E .785 Low (0) 24 (27.3) 16 (25.4) 7 (36.8) 1 (16.7) Intermediate (1) 44 (50) 32 (50.8) 9 (47.4) 3 (50) High (2-3) 20 (22.7) 15 (23.8) 3 (15.8) 2 (33.3) Missing, n 28 16 5 7 IGHV mutational status <.01 Mutated 90 (62.5) 58 (74.4) 22 (71) 10 (28.6) Unmutated 54 (37.5) 20 (25.6) 9 (29) 25 (71.4) Undetermined, n 9 7 — 2 Missing, n 1 — 1 ZAP-70 .121 <20% 88 (74.6) 54 (78.3) 20 (80) 14 (58.3) ≥20% 30 (25.4) 15 (21.7) 5 (20) 10 (41.7) Missing, n 36 16 6 14 CD38 .011 <30% 117 (84.2) 69 (90.8) 24 (85.7) 24 (68.6) ≥30% 22 (15.8) 7 (9.2) 4 (14.3) 11(31.4) Missing, n 15 9 3 3 FISH analysis 17 deletion 11 (7.9) 6 (8.1) 3 (10.3) 2 (5.4) 11q deletion 14 (10) 8 (10.8) 3 (10.3) 3 (8.1) 13q deletion 77 (55) 38 (51.4) 21 (72.4) 18 (48.6) Trisomy 12 26 (18.6) 18 (24.3) 5 (17.2) 3 (8.1) Missing, n 14 11 2 1 P values are for comparisons across the 3 risk groups determined by the CLL15 score. CLL-IPI, International Prognostic Index for CLL; FISH, fluorescence in situ hybridization. 8 NOVEMBER 2022 • VOLUME 6, NUMBER 21 CLL15 ASSAY FOR TIME TO PROGRESSION IN CLL 5765 Table 1 (continued) CLL15 categories Total cohort Low-risk group Intermediate-risk group High-risk group (n = 154), n (%) (n = 85), n (%) (n = 31), n (%) (n = 38), n (%) P value Complex karyotype (≥3 abnormalities) .877 No 63 (90) 42 (89.4) 14 (93.3) 7 (87.5) Yes 7 (10) 5 (10.6) 1 (6.7) 1 (12.5) Missing, n 84 38 16 30 TP53 mut .009 No 92 (92) 61 (98.4) 20 (83.3) 11 (78.6) Yes 8 (8) 1 (1.6) 4 (16.7) 3 (21.4) Missing, n 54 23 7 24 NOTCH1 mut .351 No 86 (82.7) 49 (79) 23 (92) 14 (82.4) Yes 18 (17.3) 13 (21) 2 (8) 3 (17.6) Missing, n 50 23 6 21 SF3B1 mut .066 No 92 (94.8) 61 (98.4) 21 (91.3) 10 (83.3) Yes 5 (5.2) 1 (1.6) 2 (8.7) 2 (16.7) Missing, n 57 23 8 26 MYD88 mut .395 No 85 (94.6) 60 (96.8) 21 (91.3) 7 (87.5) Yes 5 (5.4) 2 (3.2) 2 (8.7) 1 (12.5) Missing, n 61 23 8 30 Median follow-up, months (mo) 43.8 43.6 43.8 45.9 .61 P values are for comparisons across the 3 risk groups determined by the CLL15 score. CLL-IPI, International Prognostic Index for CLL; FISH, fluorescence in situ hybridization. with the highest impact in TtFT, we performed a least absolute Digital gene expression for 178 genes of interest and 22 house- shrinkage and selection operator regression using package glmnet keeping genes (supplemental Table 1) was determined in 156 in the R software to build the most parsimonious multivariate samples from the training cohort. Adequate gene expression was model. Imputation of random missing values was carried out via the obtained in 154 (99%) samples. Two samples (1%) with not mice R package (supplemental Table 3). The median follow-up was enough quality for expression testing were excluded from the calculated using the reverse Kaplan-Meier method. All analyses analysis. were performed using the R statistical software version 3.6.2. The expression of 76 genes was significantly associated with TtFT in univariate Cox regression analysis (adjusted P value controlling Results for false discovery rate [FDR] < .05), and 88 with FDR < .1. A total Generation of a prognostic model based on gene of 46 genes (FDR < .1) met the prespecified inclusion criteria and were selected for further analysis (see “Methods”). Among them, a expression: the CLL15 assay total of 15 genes (MYC, ITGA4, CERS6, ZNF471, ZNF667, The training cohort was comprised of 156 patients with previously SEPT10P1, ZAP70, LTK, CCL3, CNR1, EGR2, TNF, IL4R, FGL2, untreated CLL. The median age of the series was 66 years (range, PPBP) were finally selected for a prognostic model of TtFT using a 34-90 years), and 57% of the patients were men. In total, 37% of penalized Cox method. In addition, 15 housekeeping genes were samples were obtained at the time of CLL diagnosis, whereas 63% selected based on their low variance across the samples. A final were obtained during the follow-up of patients before any CLL model, named CLL15, to predict TtFT in the training cohort was treatment. The median time from CLL diagnosis to sample collec- developed using the expression of the 15 predictive genes tion was 11.9 months (95% CI, 7.1-22.6). The analysis of TtFT was normalized with the 15 housekeeping genes (Figure 1). Subse- calculated from the date of collecting the sample to the date of quently, a linear equation comprising log-transformed, normalized treatment onset. The main clinical and biological characteristics of gene expression levels of the 15 genes multiplied by their the series are shown in Table 1. Ninety-two cases (59%) were respective regression coefficients was established and calculated IGHV mutated, 54 cases (35%) were IGHV unmutated, and 9 for each patient of the training cohort to obtain the CLL15 score. cases (6%) were undetermined because of polyclonal, unproduc- The C-statistic for the model was 0.77. Figure 2A shows the shape tive, or biclonal rearrangement. In 1 case, no IGHV mutational data of the association between the CLL15 score and TtFT risk after were obtained. relaxing the linearity assumption for continuous variables. As a 5766 ABRISQUETA et al 8 NOVEMBER 2022 • VOLUME 6, NUMBER 21 115_CLL17P - RCC 100_150189 - RCC 101_130121 - RCC 103_140377 - RCC 106_140317 - RCC 107_150009 - RCC 108_160149 - RCC 109_150202 - RCC 110_120003 - RCC 111_110111 - RCC 113_140129 - RCC 116_140036 - RCC 120_150258 - RCC 122_150385 - RCC 125_180057 - RCC 139_130283 - RCC 142_120077 - RCC 152_150441 - RCC 10_120106 - RCC 11_120010 - RCC 12_160827 - RCC 13_130299 - RCC 14_120014 - RCC 15_110133 - RCC 16_130354 - RCC 17_110043 - RCC 18_120072 - RCC 19_150052 - RCC 20_150291 - RCC 21_140210 - RCC 22_150150 - RCC 23_150555 - RCC 24_140106 - RCC 25_130297 - RCC 26_130249 - RCC 27_130076 - RCC 28_160600 - RCC 29_160109 - RCC 30_140416 - RCC 31_140162 - RCC 32_110074 - RCC 33_120035 - RCC 34_150657 - RCC 35_170600 - RCC 36_160963 - RCC 37_140121 - RCC 38_150246 - RCC 39_150370 - RCC 40_120183 - RCC 41_120079 - RCC 42_110012 - RCC 43_140358 - RCC 44_140196 - RCC 45_140092 - RCC 46_140091 - RCC 47_150434 - RCC 48_150019 - RCC 49_160501 - RCC 50_160958 - RCC 51_140132 - RCC 52_140425 - RCC 53_120158 - RCC 54_140344 - RCC 55_110151 - RCC 56_150146 - RCC 58_120054 - RCC 59_130298 - RCC 60_150477 - RCC 61_140373 - RCC 62_140328 - RCC 63_170259 - RCC 64_160287 - RCC 65_150161 - RCC 66_150173 - RCC 67_120084 - RCC 68_150526 - RCC 69_110002 - RCC 70_120061 - RCC 71_130340 - RCC 72_140382 - RCC 73_160525 - RCC 74_150487 - RCC 75_170717 - RCC 76_120088 - RCC 77_140080 - RCC 78_110148 - RCC 79_110050 - RCC 80_120030 - RCC 81_120050 - RCC 82_140264 - RCC 83_140004 - RCC 84_160511 - RCC 85_140276 - RCC 86_160428 - RCC 88_150451 - RCC 89_150171 - RCC 90_130263 - RCC 91_160225 - RCC 92_150294 - RCC 93_120059 - RCC 95_120043 - RCC 96_160033 - RCC 97_140040 - RCC 98_140378 - RCC 99_150029 - RCC 102_31506 - RCC 104_28068 - RCC 105_30276 - RCC 112_33865 - RCC 114_27769 - RCC 117_32000 - RCC 118_28008 - RCC 119_28264 - RCC 121_30755 - RCC 123_36475 - RCC 124_27604 - RCC 126_35700 - RCC 127_30232 - RCC 128_27637 - RCC 129_31968 - RCC 131_28734 - RCC 133_29196 - RCC 135_33160 - RCC 136_34191 - RCC 137_36402 - RCC 138_31443 - RCC 140_33626 - RCC 141_28769 - RCC 143_27882 - RCC 144_28486 - RCC 145_28219 - RCC 146_34138 - RCC 147_32547 - RCC 148_30620 - RCC 149_30525 - RCC 150_31464 - RCC 151_27067 - RCC 153_34359 - RCC 57_CLL18 - RCC 130_CLL5 - RCC 132_CLL3 - RCC 134_CLL6 - RCC 154_CLL7 - RCC 1_150101 - RCC 2_150313 - RCC 3_110134 - RCC 4_160831 - RCC 6_150235 - RCC 8_161016 - RCC 9_160850 - RCC 87_34346 - RCC 94_30740 - RCC 5_27951 - RCC 7_27883 - RCC MYC ITGA4 CERS6 ZNF471 ZNF667 SEPT10P1 ZAP70 LTK CCL3 CNR1 EGR2 TNF IL4R FGL2 PPBP CLL15 Low (55%) Intermediate (20%) High (25%) Mutated IGHV Unmutated Figure 1. The gene expression–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. continuous variable, the CLL15 assay score was associated with P < .001). According to these results, the model combining gene TtFT (HR, 2.83; 95% CI, 2.17-3.68; P < .001). To better stratify the expression with the IGHV variable performed better in predicting risk of progression, the optimal thresholds for defining 3 groups TtFT than the models of gene expression and IGHV by themselves. with differentiated outcomes (TtFT) were determined using the R In the pairwise multivariate Cox models, both variables, IGHV partykit package. The low-risk group (score ≤ 2.718, comprising mutational status, and the categorized groups of progression risk 55% of the cohort) had a 5-year estimated risk of treatment initi- according to the gene expression model contributed prognostically ation of 30.5%. In the intermediate-risk group (score ≤ 3.535 and (Figure 2D; supplemental Table 4). >2.718, comprising 20% of the cohort), the 5-year estimated risk The inclusion of the CLL15 score also improved the capacity to of treatment initiation was 57.8% (HR, 2.67; 95% CI, 1.39-5.10; predict TtFT of the IPS-E score. Figure 4A shows the increment in P = .003). Finally, in the high-risk group (score > 3.535, comprising discrimination capacity in terms of C-statistic when the CLL15 25% of the cohort) the 5-year estimated risk of treatment initiation score was included in the model concurrently with the IPS-E score was 93.4% (HR, 10.9; 95% CI, 6.12-19.3; P < .001) (Figure 2B). or IGHV status. Moreover, in pairwise multivariate Cox models, the Notably, the CLL15 score exhibited a similar prognostic capacity in CLL-IPI and CLL15 also independently contributed to TtFT in the the subgroup of patients with an early clinical stage (n = 116), with training cohort, with a C-statistic of 0.73 for the CLL-IPI alone and a 5-year estimated risk of treatment initiation of 18.2%, 44.8%, and 0.81 for the combination. However, when the IPS-E score was 79.54% in the low-, intermediate-, and high-risk groups, respec- included, the information on the CLL-IPI did not improve the model tively (Figure 2C). (supplemental Table 4). Finally, the (1) CLL15 score, (2) IGHV status, and (3) IPS-E score were all independent factors that The prognostic value of the CLL15 score is improved the prediction of TtFT (all analyses of deviance pairwise independent of the IGHV mutational status and IPS-E comparison, P < .01) (Figure 4B). CLL Validation and reproducibility of the CLL15 assay We analyzed the association between the progression risk groups obtained by the CLL15 assay with known biological prognostic The CLL15 assay was then validated in cryopreserved samples factors in CLL, including the most common chromosomal alter- from 112 patients from an independent cohort from Heidelberg ations determined by FISH (del17p, del11q, and trisomy 12), the (supplemental Table 5). As a continuous variable, the CLL15 score level of protein expression of ZAP-70 and CD38 determined by was significantly associated with TtFT (HR, 1.71; 95% CI, flow cytometry, the mutations in TP53, NOTCH1, SF3B1, and 1.15-2.52; P = .007). Figure 5A shows the association between MYD88 genes, the mutational status of IGHV, CLL-IPI, and the CLL15 score and TtFT risk after relaxing the linearity assumption in IPS-E CLL score. the validation cohort. Using the preestablished cut-off in the training cohort, the assay assigned 22 (19.6%) patients to the low- In the univariate analysis, several factors such as the SF3B1 risk group, 42 (37.5%) to the intermediate-risk group, and 48 mutations, IGHV status, the expression of ZAP-70 and CD38 by (42.9%) to the high-risk group. These 3 groups presented differ- flow cytometry, clinical stage (RAI and Binet), the CLL-IPI, and the entiated outcomes with a 60-month estimated risk of treatment IPS-E score were associated with TtFT (Figure 3). In the final initiation of 16.5%, 40%, and 58.1% in the low-, intermediate-, and multivariate analysis, the CLL15 score, the IPS-E CLL, and the high-risk groups, respectively (P = .03 overall log-rank test, Binet stage were the only factors that maintained their independent Figure 5B). Moreover, as observed in the training cohort, the gene statistical significance (Figure 3). expression information, both as a continuous variable and as a risk We subsequently explored the introduction of the mutational status group, was an independent prognostic factor in the presence of of the IGHV (mutated/unmutated) as a variable in an expression IGHV mutational status (supplemental Table 6). The C-statistic for model and compared its performance with that of a previous model the IGHV mutational status and the gene expression model was of only gene expression. The C-statistic for the combined model 0.6 and 0.63, respectively, whereas the C-statistic for the com- was 0.79, and the analysis of deviance showed that the addition of bined model was 0.67. As observed in the training cohort, 3 risk IGHV status to the gene expression score (and vice versa) pro- groups were identified by combining the CLL15 score and the vided significant predictive information (analysis of deviance IGHV mutational status information (supplemental Figure 3). To 8 NOVEMBER 2022 • VOLUME 6, NUMBER 21 CLL15 ASSAY FOR TIME TO PROGRESSION IN CLL 5767 A B 3 CLL15 group HR (95%CI) p-value Low-risk (n=85) Ref. Intermediate-risk (n=31) 2.67 (1.39-5.10) 0.003 2 1.00 High-risk (n=38) 10.9 (6.12-19.3) <0.001 Proportion of event−free (TtFT) Log relative hazard 1 0.75 69.5% 0 0.50 42.2% 0.25 –1 6.6% 0.00 –2 1.5 2 2.5 3 3.5 4 4.5 0 12 24 36 48 60 72 CLL15 score Time (months) Number at risk 85 70 62 49 26 16 10 31 23 16 14 7 3 2 38 10 5 2 1 0 0 C D 1.00 1.00 Proportion of event−free (TtFT) Proportion of event−free (TtFT) 0.75 81.8% 0.75 55.2% 0.50 0.50 CLL15 group + IGHV mut Low-risk + mutated IGHV (n=58) Low-risk + unmutated IGHV (n=20) 0.25 0.25 High/Intermediate-risk + mutated IGHV (n=32) 20.5% CLL15 group (early stage) HR (95%CI) p-value High/Intermediate-risk + unmutated IGHV (n=34) Low-risk (n=79) Ref. Intermediate-risk (n=24) 2.48 (1.10-5.60) 0.03 0.00 High-risk (n=13) 7.90 (3.43-18.2) <0.001 0.00 0 12 24 36 48 60 72 0 12 24 36 48 60 72 Time (months) Time (months) Number at risk Number at risk 79 69 61 48 25 16 10 58 50 47 39 20 14 9 24 21 15 14 7 3 2 20 13 11 7 4 2 1 13 9 4 2 1 0 0 32 21 17 13 7 3 2 34 11 4 3 1 0 0 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. determine the reproducibility of the CLL15 assay, we selected Discussion 9 samples with scores distributed across the assay (low risk, intermediate risk, and high risk). The RNA from each of the samples In this study, we translated a gene expression prognostic signature was run on the CLL15 assay in triplicate, with each run performed comprising genes involved in the microenvironment activation and on a different NanoString cartridge. The results showed 100% IGHV mutational status into a test applicable to categorize patients concordance of risk-group assignment across triplicates (supple- into the differentiated risk of progression and requiring treatment mental Figure 4), with a standard deviation of 0.073 points. for their CLL. The assay demonstrated the ability to identify patients 5768 ABRISQUETA et al 8 NOVEMBER 2022 • VOLUME 6, NUMBER 21 Univariate analysis Multivariate analysis TtFT hazard ratio HR (95%CI) p-value HR (95%CI) p-value Age (10-years increment) 0.74 (0.62 − 0.9) 0.002 − − Sex (male vs. female) 1.31 (0.81 − 2.11) 0.27 − − RAI (0-1 vs. 2-4) 16.23 (9.10 – 28.97) <0.001 − − Binet (A vs. B−C) 14.35 (8.61 − 23.89) <0.001 4.39 (2.30 – 8.39) <0.001 FISH del17p (yes vs. no) 0.77 (0.33 − 1.77) 0.56 − − FISH del11q (yes vs. no) 1.81 (0.92 − 3.54) 0.08 − − FISH trisomy 12 (yes vs. no) 1.07 (0.6 − 1.89) 0.82 − − TP53 status (mut vs. WT) 1.91 (0.87 − 4.21) 0.11 − − NOTCH1 status (mut vs. WT) 1.34 (0.75 − 2.37) 0.32 − − SF3B1 status (mut vs. WT) 5.9 (3.38 − 10.3) <0.001 − − IGHV status (mut vs. unmut) 0.23 (0.14 − 0.37) <0.001 0.83 (0.45 – 1.52) 0.54 ZAP-70 (>20% vs. <20%) 2.57 (1.6 − 4.13) <0.001 1.38 (0.85 – 2.26) 0.19 CD38 (>30% vs. <30%) 3.1 (1.83 − 5.28) <0.001 − − CLL-IPI score 1.32 (1.21 − 1.44) <0.001 − − IPS-E score 3.09 (2.4 − 3.97) <0.001 2.23 (1.59 – 3.12) <0.001 CLL15 score 2.83 (2.17 − 3.68) <0.001 1.83 (1.32 – 2.56) <0.001 0.07 0.25 0.5 1 2 4 15 Hazard ratio Figure 3. Univariate and multivariate analysis for TtFT according to prognostic factors in CLL. at a high risk of requiring treatment in a short time or with an as NanoString nCounter, capable of digital, direct quantification on extremely stable disease. a real-time basis for individual patients, allows the attainment of gene expression analysis in a clinical setting.27,28 In this regard, we Based on the enormous advances in the biology and treatment of demonstrated the clinical strength and reproducibility of the CLL15 CLL, classical staging systems have been complemented by a assay in an independent cohort of previously untreated patients plethora of new prognostic parameters based on CLL genetics and with CLL and its analytical reproducibility by showing a very low biology, including gene expression profiles.14,15,37,38 Despite the variability across repeated measurements. fact that gene expression profiles have been strongly correlated with the clinical course of the patients,7,8,17-26 their translational Several in vitro and in vivo data indicate that CLL is a malignancy value in clinical practice has been difficult to implement due to highly dependent on microenvironment signals for survival and methodological reasons. The recent advent of new platforms such proliferation, with BCR signaling being the most prominent A B 76.6% CLL15 Analysis of deviance (p-values) Complex model 68.5% IGHV - CLL15 score + IGHV CLL15 score + IPS-E status score 79.3% IPS-E IGHV status <0.001 X CLL15 + 79.8% IGHV Simple IPS-E score X <0.001 CLL15 + 85.4% model IPS-E CLL15 score 0.003 <0.001 0 50 60 70 80 90 100 C-statistics 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. 8 NOVEMBER 2022 • VOLUME 6, NUMBER 21 CLL15 ASSAY FOR TIME TO PROGRESSION IN CLL 5769 A B 1.00 1 83.5% Proportion of event−free (TtFT) 0.75 0 Log relative hazard 60% 0.50 –1 41.9% 0.25 –2 CLL15 group HR (95%CI) p-value Low-risk (n=22) Ref. Intermediate-risk (n=42) 2.69 (0.77-9.39) 0.12 High-risk (n=48) 4.27 (1.29-14.1) 0.02 0.00 Overall log-rank test 0.03 1.5 2 2.5 3 3.5 4 4.5 0 12 24 36 48 60 72 CLL15 score Time (months) Number at risk 22 17 15 8 1 1 0 42 26 17 13 9 4 0 48 28 23 16 8 3 0 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. pathway activated in CLL cells isolated from lymph nodes.25 The the CLL15 assay was highly discriminative for the TtFT, with a role of the microenvironment in CLL pathogenesis has been rein- C-statistic of 0.85. It appears that combining a more clinical–based forced when molecules targeting CLL-microenvironment interac- score, such as the IPS-E, with a molecular score (CLL15) could tions have shown unprecedented therapeutic results.30,31 The increase the accuracy of both models. Unfortunately, the IPS-E score CLL15 assay included genes coding for cytokines, chemokines, was not available for the validation cohort and this comparison could and cytokines receptors such as CCL3, TNF, PPBP, and IL4R; not be validated in this cohort. integrins such as ITGA4; and transcription regulatory factors such On the other hand, the combination of IGHV and CLL15 also as MYC and EGR2, which are involved in microenvironment acti- improved the predictive capacity of the model. Three clearly vation in different studies in CLL.23-26,39-41 In addition, genes pre- different risk groups were identified after combining the CLL15 and viously reported to be differentially expressed according to the IGHV status. However, a limited improvement of the C-statistic was IGHV mutational status, including CERS6, CNR1, FGL2, LTK, observed, and the lower statistical power in the validation cohort SEPT10P1, ZAP70, ZNF471, and ZNF667, were also selected in did not allow for the validation of all findings. the CLL15 assay.17,18,24,26,41-44 Notably, the levels of expression of the aforementioned genes could also be regulated in microenvi- Currently, one of the moving fields is the possibility of early treatment of ronment activation processes.18,25,45 Thus, ZAP-70 expression has patients at early stages that are likely to progress within a short period.5 been associated with enhanced and prolonged BCR signaling,46,47 The selection of these patients is usually based on standard prognostic higher responsiveness to chemokines [56-58], and enhanced scores. The usage of more accurate methods for prognostication, such migration of CLL cells,48,49 reinforcing the notion that increased as the CLL15 score, should allow for better identification of patients ZAP-70 expression is associated with a more aggressive clinical with an increased risk of early progression and thus support future trials course of patients with CLL.37,50,51 based on risk-adapted therapeutic intervention. It is worth mentioning that the CLL15 signature kept its predictive In conclusion, the biological prognostication of CLL relies on the value independent of the IGHV mutational status, the CLL-IPI, and the use of genetic aberrations together with the mutational status of IPS-E CLL score. More importantly, the inclusion of the CLL15 score IGHV. Unfortunately, the use of gene expression profiles has been improved the discrimination capacity to predict TtFT when IGHV or difficult owing to its technical difficulties and reproducibility, pre- IPS-E was included in the model, suggesting that the CLL15 signa- cluding its use in clinical practice. The use of newer and more ture could complement the prognostic value of these other variables. reproducible methods to assess gene expression could round off In addition, the combination of the CLL15 and CLL-IPI provided well-established prognostic parameters, appraising the entire bio- independent predictive information; however, with the inclusion of the logical profile for the prognostication of patients with CLL. The IPS-E score, the information of the CLL-IPI did not contribute prog- study presented herein successfully translates previously nostically to the model. In this sense, the combination of the IPS-E and described gene expression signatures with strong prognostic value 5770 ABRISQUETA et al 8 NOVEMBER 2022 • VOLUME 6, NUMBER 21 into a new gene expression–based assay, the CLL15, applicable in Authorship the routine diagnostic setting. Contribution: P.A., T.Z., M.C., and F.B. designed and supervised this work; P.A., M.C., J.L., T.Z., M.A., M.G., G.I., S.B., and A.M.-N. Acknowledgments provided samples; D.M., J.C., J.B., and B.T.-V. performed experi- The authors thank the patients for their blood donations. The ments; P.A., G.V., M.C., and F.B. analyzed and interpreted the data; authors would also like to thank the Cellex Foundation for providing P.A., G.V., M.C., and F.B. wrote the manuscript; and all authors research facilities and equipment and the CERCA Programme / revised the manuscript. Generalitat de Catalunya for institutional support. Part of the statistical analysis was performed in the Statistics and Bioinfor- Conflict-of-interest disclosure: P.A. received honoraria from matics Unit (UEB) of the Vall Hebron Hospital Research Institute. Janssen, Roche, Celgene, AbbVie, and AstraZeneca. G.V. received This work was supported by research funding from the research honoraria for speaker activities from MSD and an advisory Asociación Española Contra el Cáncer grant [5U01CA157581- role from AstraZeneca. M.A. received honoraria for speaker acti- 05, ECRIN-M3 - A29370] and in part by the Instituto de Salud vities from AstraZeneca, an advisory role from Janssen, and non- Carlos III, Fondo de Investigaciones Sanitarias [PI17/00950, M.C., financial support from Janssen, and AbbVie. A.M.-N. received PI17/00943, F.B, PI18/01392, P.A.], and the Spanish Ministry of honoraria from Janssen, Roche, Takeda, Gilead, AbbVie, and Cel- Economy and Competitiveness [CIBERONC-CB16/12/00233], gene for speaker activities and from Janssen, Takeda, Gilead, the Education Council or Health Council of the Junta de Castilla Kiowa Kirin, AstraZeneca, and Beigene for participating in advisory y León [GRS 2036/A/19], and Gilead Sciences [GLD15/00348]. boards. M.C. received research funding from Janssen, Roche, and This work was supported by research funding from the Asociación AstraZeneca. F.B. received honoraria and research grants from Española Contra el Cáncer grant [5U01CA157581-05, ECRIN-M3 Roche, Celgene, Takeda, AstraZeneca, Novartis, AbbVie, Lilly, - A29370] and in part by the Instituto de Salud Carlos III, Fondo de Beigene, and Janssen. The remaining authors declare no Investigaciones Sanitarias [PI17/00950, M.C., PI17/00943, F.B, competing financial interests. PI18/01392, P.A.], and the Spanish Ministry of Economy and Competitiveness [CIBERONC-CB16/12/00233], the Education ORCID profiles: D.M., 0000-0001-9421-949X; G.V., 0000- Council or Health Council of the Junta de Castilla y León [GRS 0003-4868-6585; J.L., 0000-0002-9211-0746; G.I., 0000-0003- 2036/A/19], Gilead Sciences [GLD15/00348] and Gilead Fel- 0805-9288; A.M.-N., 0000-0002-7592-6815; M.G., 0000-0001- lowships [GLD16/00144, GLD18/00047, F.B.], and Fundació la 6637-1072; M.C., 0000-0002-7732-7808; F.B., 0000-0001- Marató de TV3 [201905-30-31 F.B]. All Spanish funding was 9241-2886. cosponsored by the European Union FEDER program “Una man- Correspondence: Pau Abrisqueta, Department of Hematology, era de hacer Europa”. M.C. holds a contract from Ministerio de University Hospital Vall d’Hebron, Pssg Vall d’Hebron 119-129, Ciencia, Innovación y Universidades [RYC-2012-2018]. 08035, Barcelona, Spain; email: pabrisqueta@vhio.net. References 1. 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