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GEP for high dimensional prognostic models in CLL
cases. Future comparative studies assessing the prognostic impact of methylation markers need to include a compre- hensive genetic characterization since SF3B1 and NOTCH1 mutations were found to have independent prognostic and predictive impact for chemoimmunotherapy5 and show a heterogeneous distribution within epigenetic subgroups.29,31 In addition, the CLL8 trial design provided an ideal basis to differentiate between the prognostic and predictive value of markers and therefore to specifically assess for the prognos- tic strength of established and GEP variables. Notably, GEP variables selected in our model also reliably substituted for IGHV mutation status and showed strong prognostic impact irrespective of treatment for both PFS and OS in contrast to the epigenetic subgrouping.31
While storage and workup conditions were found to change expression levels of multiple transcripts in an RNA sequencing-based study on healthy donor samples, prog- nostic GEP variables selected in our study largely repre- sented transcripts with low reported variability.32 Stable expression of our prognostic GEP variables selected for the respective clinical endpoints is further supported since prognostic markers unaffected by surrounding conditions (e.g., chromosomal aberrations, gene mutation status) were reliably substituted in the multivariate analysis. Validation of the prognostic impact of selected GEP vari- ables was achieved in an independent data set differing with regard to storage conditions, workup and sorting of samples from a patient cohort with heterogeneous treat- ment,22 further demonstrating the prognostic robustness of selected GEP variables.
While novel compounds have revolutionized the land- scape of CLL treatment in particular for high-risk patients,10,11,12,13 the long-term benefit and treatment related toxicities still remain to be evaluated. Further, the signifi- cant economic burden may limit the access in some health- care systems.33 In this study, we were able to confirm that GEP variables can achieve a higher prognostic accuracy, bet- ter reflect IGHV sequence homology and reliably identify “discordant” patients with mutated IGHV but poor clinical
Figure 5. Combined status of LDOC1 and L3MBTL4 is correlated with IGHV sequence homology and identifies cases with “discor- dant” clinical course. The figure highlights the correlation between expression levels of LDOC1 (x-axis), L3MBTL4 (y-axis) and the immunoglob- ulin 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 expres- sion identifies “discordant” cases with mutated IGHV but poor clinical course (high expression of LDOC1 and/or L3MBTL4) and vice versa.
course and vice versa. This is especially promising since treatment with BTK inhibitors and FCR was reported with similar PFS in patients with mutated IGHV.14
Although the depth of biological characterization has reached a new dimension with the use of RNA sequencing, both array and RNA sequencing-based prognostic modeling were found to perform equally well for the prediction of major clinical endpoints.34 Studies evaluating FCR and BTK inhibitor treatment in a randomized fashion14 would pro- vide an ideal basis for marker validation using RNA sequencing and easy to apply quantitative real-time poly- merase chain reaction based approaches in parallel. Prognostic models used here may therefore hold promise for future selection, substitution and harmonization of prognostic markers, which show variable prognostic value within the respective treatment context.
Disclosures
The authors declare that there are no conflicts of interest to dis- close that interfered with the experiments and presentation of data.
Contributions
JB, AB and SS conceptualized study; JB performed expression profiling; JB, JK and AB analyzed data. Data were gathered by all authors. JB wrote the paper with input from JK, AB and SS and all authors reviewed the manuscript.
Acknowledgements
The authors thank all patients and their physicians for trial par- ticipation and donation of samples; the DCLLSG; Sabrina Schrell and Christina Galler for their excellent technical assistance; and Myriam Mendila, Nancy Valente, Stephan Zurfluh, and Jamie Wingate for their support in conception and conduct of the trial.
Funding
This work was supported by grants from BMBF (PRECISE), European Commission / BMBF (“FIRE CLL”, 01KT160), Deutsche Forschungsgemeinschaft (Sonderforschungsbereich 1074 project B1 and B2), DJCLS R 11/01, and F. Hoffmann-La Roche.
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