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Gene expression signature that predicts early molecular response failure in chronic-phase CML patients on frontline imatinib

Chung H. Kok, David T. Yeung, Liu Lu, Dale B. Watkins, Tamara M. Leclercq, Phuong Dang, Verity A. Saunders, John Reynolds, Deborah L. White and Timothy P. Hughes

Data supplements

Article Figures & Data

Figures

  • Figure 1.

    Distinct gene expression profile and enrichment of stem cells signature identified in diagnostic MNCs from patients who failed to achieve EMR. (A) The workflow of our study with regard to the discovery process, including samples from patients with EMR failure (n = 13) vs EMR achievement (n = 83), as defined by BCR-ABL1 percentage at 3 months. (B) Volcano plot demonstrating the effect of log2 fold change (FC) on the x-axis vs −log10 P value on the y-axis. Red circles indicate significant genes (FDR P < .05 and log2 FC > 0.6) with increased gene expression in the EMR failure patient group. Green circles indicate significant genes (FDR P < .05 and log2 FC < −0.6) with decreased gene expression in the EMR failure patient group. (C) Heatmap demonstrating the distinct gene expression patterns based on all significant probes (n = 502; FDR P < .05 and log2 FC > |0.6|). Orange represents increased gene expression level, and blue represents decreased gene expression level. The heatmap was generated using the pheatmap package. (D) GSEA indicates enrichment of stem cell signaling, cell cycle, and immune response/T lymphocytes in the EMR failure patient samples (BCR-ABL1 >10% IS at 3 months) compared with the EMR achievement patient samples (BCR-ABL1 ≤10% IS at 3 months). Blue bars represent cell cycle–related data sets. Red bars represent stem cell–related data sets. Green bars represent immune response/T lymphocyte–related signatures. Regarding normalized enrichment score (NES), positive score indicates positive enrichment in samples from patients who failed to achieve EMR, and negative score indicates enrichment in samples from patients who achieved EMR. (E) Boxplot displaying the differential blast percentage counts at diagnosis, indicating a significantly higher percentage in the samples collected from patients who failed to achieve EMR. Only 91 patients had differential blast percentage counts at diagnosis information available for analysis. (F) Boxplot displaying the lymphocyte percentage counts at diagnosis, indicating a significantly lower percentage in the EMR failure patient sample group. Only 94 patients had lymphocyte percentage counts at diagnosis information available for analysis. Statistical analysis was performed using the Mann-Whitney U test.

  • Figure 2.

    Prognostic significance of gene expression–based classification. (A) Bar plot displays the genes that are important contributors to the EMR failure prediction model. The classifier parameters were determined by fivefold cross-validation, yielding a binary classification model that predicts risk of EMR failure based on expression levels of 17 genes. (B) Scatter plot demonstrates the risk score by our predictive model for all 88 patients (EMR achievement or EMR failure) in the independent validation cohort. By default, high risk of EMR failure (HR-GES) was defined as risk score >0.5, and low risk of EMR failure (LR-GES) was defined as risk score ≤0.5. According to this model, 9 (10%) and 79 patients (90%) were classified as HR-GES and LR-GES, respectively. (C) Bar plot of patient samples assigned as HR-GES (n = 9), where 78% failed to achieve EMR compared with 5% of patient samples assigned as LR-GES (n = 79) in the independent validation cohort. Statistical analysis was performed using Fisher’s exact test. (D) Bar plot demonstrating high-risk patient group classified by our predictive model (left) and EMR failure patient group (classified by BCR-ABL1 percentage at 3 months; right). HR-GES indicates a higher BC progression rate compared with low-risk patient group classified by our model or EMR achievement patient group (classified by BCR-ABL1 level at 3 months), respectively.

  • Figure 3.

    The high-risk patient group classified by our predictive model had inferior MRs compared with the low-risk patient group in the validation cohort. (A) The high-risk (HR-GES) patient group classified by our predictive model (n = 9) had significantly inferior cumulative incidence (44%) of MMRs (>3 log reduction of BCR-ABL1 transcript value) by 24 months compared with the low-risk (LR-GES) patient group (78%; n = 79). (B) The EMR failure patient group defined by BCR-ABL1 percentage at 3 months (n = 11) had significantly inferior cumulative incidence of MMRs (>3 log reduction of BCR-ABL1 transcript value) by 24 months (45%) compared with the EMR achievement patient group (79%; n = 77). (C) The high-risk patient group classified by our predictive model (n = 9) had significantly inferior cumulative incidence of deep MR (MR4.5; >4.5 log reduction of BCR-ABL1 transcript value) by 5 years (0%) when compared with the low-risk patient group (63%; n = 79). (D) The EMR failure patient group defined by BCR-ABL1 percentage at 3 months (n = 11) had significantly inferior cumulative incidence of MR4.5 by 5 years (30%) compared with the EMR achievement patient group (61%; n = 77). All statistical analyses were performed using the Fine and Gray test.

  • Figure 4.

    The high-risk patient group classified by our predictive model demonstrated inferior clinical outcomes compared with the low-risk patient group in the validation cohort. (A) The high-risk (HR-GES) patient group classified by our predictive model (n = 9) had significantly inferior EFS by 5 years (53%) compared with the low-risk (LR-GES) patient group (83%; n = 79). (B) The EMR failure patient group classified by BCR-ABL1 percentage at 3 months (n = 11) had inferior EFS by 5 years (59%) compared with the EMR achievement patient group (83%; n = 77). (C) The high-risk patient group classified by our predictive model (n = 9) had significantly inferior FFS by 5 years (44%) when compared with the low-risk patient group (72%; n = 79). (D) The EMR failure patient group classified by BCR-ABL1 percentage at 3 months (n = 11) had inferior FFS by 5 years (45%) when compared with the EMR achievement patient group (73%; n = 77). All statistical analyses were performed using the log-rank test.

  • Figure 5.

    Our predicted HR-GES patient group had a much lower risk of EMR failure if treated with nilotinib upfront. (A) Bar plot demonstrates comparison of the EMR failure rate in patient samples assigned as high risk (HR-GES) by our predictive model if treated with imatinib (78%; n = 9) vs nilotinib upfront (10%; n = 20). (B) Bar plot demonstrates comparison of the EMR failure rate in patient samples assigned as low risk (LR-GES) by our predictive model if treated with imatinib (5%; n = 79) or nilotinib upfront (3%; n = 112). (C) Of the 132 patients treated with nilotinib upfront, there were 75 patients who had 24-month follow-up MR data. The high-risk patient group classified by our predictive model (n = 9) had inferior cumulative incidence of deep MR (MR4.5; >4.5 log reduction of BCR-ABL1 transcript value) by 24 months (0%) when compared with the low-risk patient group (29%; n = 66; P = .067). Statistical analysis was performed using the Fine and Gray test. (D) The high-risk patient group classified by our predictive model (n = 9) had no statistically significant difference in cumulative incidence of MMRs (>3 log reduction of BCR-ABL1 transcript value) by 24 months (78%) compared with the low-risk patient group (85%; n = 66; P = .55). Statistical analysis was performed using the Fine and Gray test.

Tables

  • Table 1.

    Patient characteristics for discovery and validation cohorts

    VariableDiscovery cohort (n = 96)Validation cohort (n = 88)P
    Age, y .669
     Median4850.5
     Range17-7717-80
    Sex .552
     Female38 (39.6)39 (44.3)
     Male58 (60.4)49 (55.7)
    BCR-ABL1 at 3 mo, % 1
    <1083 (86.5)77 (87.5)
     >1013 (13.5)11 (12.5)
    BCR-ABL1 transcript type .084
     b2a238 (39.6)36 (40.9)
     b2a31 (1.0)0 (0.0)
     b3a232 (33.3)39 (44.3)
     Both25 (26.0)12 (13.6)
     e1a20 (0.0)1 (1.1)
    Sokal score .458
     High15 (16.9)19 (21.8)
     Intermediate27 (30.3)30 (34.5)
     Low47 (52.8)38 (43.7)
    ELTS .442
     High7 (7.9)12 (13.8)
     Intermediate23 (25.8)20 (23.0)
     Low59 (66.3)55 (63.2)
    BC progression .447
     Yes5 (5.2)2 (2.3)
     No91 (94.8)86 (97.7)
    MMR by 24 mo .279
     Yes79 (82.3)66 (75.0)
     No17 (17.7)22 (25.0)
    MR4.5 by 5 y .298
     Yes59 (61.5)47 (53.4)
     No37 (38.5)41 (46.6)
    • Data are n (%) unless otherwise noted.

  • Table 2.

    Detailed information about patients misclassified by our predictive model

    Patient IDBCR-ABL1 % at diagnosisBCR-ABL1 % at 3 moSokal scoreELTSBCR-ABL1 transcript typeTKIsMMR by 12 moMR4.5 by 5 yWithdrawal
    EMR failure patients (n = 4) classified as low risk
     145* 5514IntIntb2a2IMN/AN/AYes
     1098531HighHighb3a2IM, NILNoYesNo
     10212413LowLowb3a2IMYesNoNo
     1707318LowLowb3a2IM, NILNoYesNo
    EMR achievement patients (n = 2) classified as high risk
     183891.50IntIntb2a2IM, NILNoNoNo
     159866.90HighHighb3a2IM, NILNoNoNo
    • IM, imatinib; int, intermediate; N/A, not applicable; NIL, nilotinib.

    • * Withdrawn at 6 months because of poor patient compliance.