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J. Bloehdorn et al.
RGS1 was homogeneously distributed across the expres- sion range, LDOC1 and L3MBTL4 expression showed a bimodal distribution (Online Supplementary Figure S3). When evaluating expression level distributions of RGS1, LDOC1 and L3MBTL4 in relation to genetic variables, we could not identify an exclusive association with known prognostic factors (Figure 3; Online Supplementary Table S4A to D).
In order to elucidate the biologic context from which the prognostic impact of these three genes may derive, we dichotomized patient samples regarding the upper and lower quartile of RGS1, LDOC1 and L3MBTL4 expression and assessed the differential expression of associated genes. Differentially expressed genes with a false discov- ery rate (FDR) of <0.01 and a fold-change (FC) of >1.5 were assessed for overlaps of the respective expression signatures (Figure 4A). Only 12 genes were overlapping between all three gene-specific comparisons (Figure 4A). Expression signatures associated with RGS1 were highly distinct from the other profiles and showed only nine of 341 genes exclusively overlapping with the LDOC1 spe- cific signature. Conversely, 51 of 69 genes contained in the L3MBTL4 signature exclusively overlapped with the LDOC1 signature and therefore support a similar biologic context. Genes contained in different signatures showed highly correlated expression profiles (Figure 4B). LDOC123 and other genes overlapping for the L3MBTL4 and LDOC1 signature, such as LPL or CRY1, were previously reported as surrogate markers for the IGHV mutation sta- tus.24,25 We specifically investigated ZAP70 in this context, since it has also been identified as a surrogate marker for the IGHV mutation status.25,26,27 While ZAP70 had a fold- change lower than the previously set cut-off (FC>1.5), we found a highly significant (q<1x10-7) association with LDOC1 and L3MBTL4 (Figure 4C). Provided that LDOC1 and L3MBTL4 expression levels did not show an exclusive association with the IGHV mutation status (Figure 3; Online Supplementary Table S4A to D), we wondered if the combined status of these two genes may explain the observed similarities. Notably, expression of LDOC1 and L3MBTL4 was highly correlated with each other and the combination of both variables reliably identified the majority of cases with IGHV homology <98% (Figure 5). However, we observed several “discordant” cases with mutated IGHV and high expression levels of LDOC1 and L3MBTL4 or IGHV unmutated cases with low expression levels (Figure 3; Figure 5). Provided the fact that these con- tinuous variables were selected due to the higher prognos- tic accuracy instead of the categorical IGHV mutation sta- tus, these markers therefore better mirror prognostic effects and the related biology of a variable sequence homology, especially in “discordant” cases.
Discussion
In the presented study, we evaluated the significance of GEP as a means for prognostic modeling in CLL. The CLL8 study cohort provides a valid basis for this as it was designed as a large international, multi-center phase III study defining current standard treatment, with full genetic characterization and long follow-up. Importantly, CD19+ purified tumor cells were procured at enrollment allowing valid GEP analysis.
While GEP was unable to improve prediction when used in addition to confirmed prognostic variables, GEP substi-
tuted for many of these variables when tested in direct comparison in the equally penalized model and reliably predicted OS and PFS, similar to models integrating only confirmed prognostic variables. Furthermore, for the prog- nostication of PFS, GEP was able to compensate for missing genetic information in the subgroup with late progression events.
High prediction accuracy for late progression and confir- mation of the independent prognostic value for previously reported high-risk markers,4,5,28 which were selected in the equally penalized model, implies that GEP-based prognos- tication can primarily substitute for intermediate and low- risk prognostic variables. However, GEP-based prognostic modeling was also able to substitute for “unmutated IGHV”, one of the most important variables with negative prognostic impact on OS and PFS.1,6,7,28
GEP variables selected for PFS and OS in the equally penalized models were largely heterogeneous, a finding that may reflect both methodological and biological differ- ences when modeling these endpoints. Conversely, we identified RGS1, LDOC1 and L3MBTL4 to have prognostic value both for PFS and OS. While the combined expression of LDOC1 and L3MBTL4 was highly associated with IGHV homology and therefore may be viewed as surrogate mark- er of the IGHV mutation status at first, one has to consider that both genes were selected in the prognostic model instead of the IGHV mutation status. This indicates that these genes and the associated biology have a considerable impact on the prognosis and not merely substitute for the IGHV mutation status.
This study further demonstrates the potential of GEP to reduce biologic dimensionality. As such, chromosomal aberrations affecting a multitude of genes, also if minimally deleted regions only are considered, can be replaced by less than a dozen genes. The fact that the genes contained in the prognostic GEP scores were not located on recurrently affected chromosomal regions indicates that the deregulat- ed expression does not derive from a mere gene dosage effect but represents a convergence of various biologic traits. Genes of the identified signatures likely constitute important elements in overactive signaling cascades impact- ing on the clinical course. In addition, GEP variables repre- sent continuous variables and therefore may hold more potential to fine-tune prognostic modeling in contrast to categorical variables such as aberrations and mutations.
The efficacy resulting from the addition of rituximab to FC treatment and substantial benefit for patients with dis- tinct genetic features leading to long-term disease control and OS has been confirmed recently in a long-term follow- up analysis.1 Notably, prognostic variables selected in the equally penalized model or the GEP signature estimated the clinical course of long-term PFS within this cohort better compared to the model using only genetic factors or param- eters previously identified to characterize such patients.1
Future studies will provide insight, if prognostic models including GEP also hold advantage over recently reported prognostic models using epigenetic subgrouping.29,30,31 Patients with DNA methylation profiles reflecting memory B-cell-like CLL were reported to strongly benefit from treat- ment with chemoimmunotherapy on two phase II trials.31 A major strength of our study was the possibility to exclu- sively use CD19+ sorted patient samples from a random- ized phase III trial and extensive characterization for estab- lished prognostic variables, including availability of the TP53, SF3B1 and NOTCH1 mutation status in >95% of
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haematologica | 2022; 107(3)