Integrative prognostic models predict long-term survival after immunochemotherapy in chronic lymphocytic leukemia patients

Abstract

Chemoimmunotherapy with fludarabine, cyclophosphamide and rituximab (FCR) can induce long-term remissions in patients with chronic lymphocytic leukemia. Treatment efficacy with Bruton's tyrosine kinase inhibitors was found similar to FCR in untreated chronic lymphocytic leukemia patients with a mutated immunoglobulin heavy chain variable (IGHV) gene. In order to identify patients who specifically benefit from FCR, we developed integrative models including established prognostic parameters and gene expression profiling (GEP). GEP was conducted on n=337 CLL8 trial samples, "core" probe sets were summarized on gene levels and RMA normalized. Prognostic models were built using penalized Cox proportional hazards models with the smoothly clipped absolute deviation penalty. We identified a prognostic signature of less than a dozen genes, which substituted for established prognostic factors, including TP53 and IGHV gene mutation status. Independent prognostic impact was confirmed for treatment, β2-microglobulin and del(17p) regarding overall survival and for treatment, del(11q), del(17p) and SF3B1 mutation for progression-free survival. The combination of independent prognostic and GEP variables performed equal to models including only established non-GEP variables. GEP variables showed higher prognostic accuracy for patients with long progression-free survival compared to categorical variables like the IGHV gene mutation status and reliably predicted overall survival in CLL8 and an independent cohort. GEP-based prognostic models can help to identify patients who specifically benefit from FCR treatment. The CLL8 trial is registered under EUDRACT-2004- 004938-14 and clinicaltrials gov. Identifier: NCT00281918.

Figures

Figure 1.

Prediction error estimates for prognostic model combinations. Prediction error curves for combinations of prognostic variables in models are shown for overall survival (OS) (A) and progression-free survival (PFS) (B). Combinations of prognostic variables contain the confirmed prognostic variables, as used in the reference model (age, sex, study medication, Eastern Cooperative Oncology Group [ECOG], log white blood cells [WBC], β2-microglobulin [β2- m], log thymidine kinase [TK], IGHV mutation status, del(11q), del(13q), del(17p), trisomy 12, TP53 mutation, NOTCH1 mutation, SF3B1 mutation) and gene expression profiling (GEP) variables. Prognostic GEP variables were selected in addition to (fixed model) or instead of (equally penalized model) the confirmed prognostic variables. In a separate approach prognostic GEP variables were selected in addition to (fixed model) or instead of (equally penalized model) non-genetic prognostic variables (only age, sex, study medication, ECOG, log WBC, log TK, β2-m). GEP variables selected in the fixed or equally penalized model largely overlap with the full prognostic gene signature (Online Supplementary Table S2), which is separately used in the “GEP data only” prediction error curve. Combination of prognostic variables selected in the equally penalized model performed highly similar to the model containing only confirmed prognostic variables. Strong overlap was found for prediction error curves represented by the red and blue solid lines.

Figure 2.

Conditional Kaplan-Meier survival estimates illustrate the distribution for overall survival and progression-free survival within the different prediction models. Kaplan-Meier estimates were generated for the lowest, the median, and the highest observed values of the prognostic variable combinations. Kaplan-Meier estimates illustrate overall survival (OS) (A, C and E) and progression-free survival (PFS) (B, D and F) with regard to the “reference model” (confirmed prognostic variables only, A and B), the “equally penalized model” (confirmed prognostic variables and GEP equally penalized, C and D) and prognostic GEP signatures only (as represented in the Online Supplementary Table S2A and B) (E and F).

Figure 3.

Association of RGS1, LDOC1 and L3MBTL4 with genetic variables. Boxplots showing distribution for log2 expression of genes selected for both overall survival (OS) and progressionfree survival (PFS), namely RGS1, LDOC1 and L3MBTL4. LDOC1 and L3MBTL4 show a bimodal distribution. Distribution of the three genes was not exclusively associated with distinct genetic variables.

Figure 4.

Assessment of genes showing concordant or discordant expression with RGS1, LDOC1 and L3MBTL4. (A) Venn diagram illustrating overlaps for differentially expressed genes (fold-change [FC] >1.5; false discovery rate [FDR] <0.01) between patient samples with either high or low expression (upper vs. lower quartile) for RGS1, LDOC1 and L3MBTL4. (B) Heatmap showing clustered expression pattern (Pearson correlation and average linkage) of 12 genes found in all three gene specific signatures and heatmap showing expression pattern of 51 genes found in gene specific signatures of LDOC1 and L3MBTL4. (C) Scatter plots for ZAP70 expression with regard to groups showing high and low LDOC1 and L3MBTL4 expression (upper vs. lower quartile).

Figure 5.

Combined status of LDOC1 and L3MBTL4 is correlated with IGHV sequence homology and identifies cases with “discordant” clinical course. The figure highlights the correlation between expression levels of LDOC1 (x-axis), L3MBTL4 (y-axis) and the immunoglobulin heavy chain variable (IGHV) gene sequence homology (color coded). Cases with IGHV sequence homology <98% are indicated in blue, cases with IGHV sequence homology ≥98% are indicated in red. LDOC1 and L3MBTL4 expression identifies “discordant” cases with mutated IGHV but poor clinical course (high expression of LDOC1 and/or L3MBTL4) and vice versa.