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P. Baliakas et al.
approach, namely binary recursive partitioning, which offers a different framework, thereby conveying a hierar- chical order of importance and classification for the evalu- ated prognostic factors.
Admittedly, despite providing a robust risk stratification scheme, the prognostic indices proposed here will not solve the problem of outliers, while they also overlook the potential effect of other variables with proven prognostic significance, e.g. cytogenetic complexity or methylation signatures. Thus, they cannot be considered the last word in biomarker-orientated risk stratification for TTFT. They do, however, highlight the need for further studies, while providing the conceptual frame of compartimentalization. It should be further emphasized that our approach is built on the pivotal role of IGHV SHM status in the prognostic setting, which appears to be less important in the era of novel agents, namely BTK and BCL2 inhibitors, where response rates seem to be similar between M-CLL and U- CLL.46,47
Recently, the International Prognostic Index for patients with CLL (CLL-IPI) was developed for assessing overall survival (OS).7 This has provided a robust prognostic clas- sification scheme as it includes well-characterized patients followed in the context of clinical trials. A caveat of CLL-IPI concerns the fact that the evaluated patients had been treated with various regimens in the context of different clinical trials. Moreover, CLL-IPI does not allow the identification of distinct groups within each SHM cat- egory. For example, following the CLL-IPI score, a young (<65 years), early-stage patient, negative for TP53abn and belonging to M-CLL would never be characterized as very high risk. It is important to note, therefore, that the clinically aggressive stereotyped subset #2 would be over- looked by the CLL-IPI as it largely falls into the category of M-CLL cases lacking TP53abn, with 60% of patients also under 65 years of age. This is not a trivial issue, con- sidering that subset #2 accounts for up to 5% of all CLL requiring treatment and, therefore, is equal in size to the CLL-IPI very high risk group with very limited if any overlap.30 A final, more general concern is that CLL-IPI was developed based on the analysis of cases treated prior to the introduction of novel therapies, which are likely to change the treatment expectations and OS in CLL, thus eventually creating the need for new predictive schemes.48
Our study identified subset #2 membership and SF3B1 mutations as prognostically important biomarkers for early-stage M-CLL and U-CLL, respectively. In contrast, other recurrent gene mutations such as NOTCH1 or BIRC3 failed to reach significance even in univariable analysis. Interestingly, SF3B1 mutations are remarkably enriched within subset #2 (approx. 50% of cases harbor a mutation), however, their impact within this very aggres- sive subset remains equivocal.30,32 Overall, these results
emphasize the value of investigating IG sequences for stereotyped subset #2 membership (easily determined through the use of a free online tool available at: http://bat.infspire.org/arrest/assignsubsets/49) and searching for SF3B1 mutations in routine clinical practice as this would enable a more accurate assessment of prognosis (TTFT) at diagnosis of CLL.
In conclusion, we propose a novel approach to prognos- tic assessment in CLL grounded on the fact that not all CLL are equal, but instead that M-CLL and U-CLL cate- gories are fundamentally different regarding their ontoge- ny and molecular landscape, at least for early-stage patients (Figure 7). Our results support that compartmen- talizing CLL with the BcR IG as the starting point allows accurate prognostication in early-stage CLL. This further shows that the relative weight of well established prog- nostic indicators differs based on the immunogenetic fea- tures of each individual case. From a broader perspective, such compartmentalized approaches might prove relevant in other B-cell lymphomas as well e.g. diffuse large B-cell lymphoma where different biomarkers are emerging as prognostically relevant for the activated B-cell or the ger- minal center subtype, respectively,50 that display distinct immunogenetic features and signaling signatures.50
Funding
This work was supported in part by the Swedish Cancer Society, the Swedish Research Council, the Knut and Alice Wallenberg Foundation, Karolinska Institutet, Stockholm, the Lion’s Cancer Research Foundation, Uppsala, the Marcus Borgström Foundation and Selander’s Foundation, Uppsala; H2020 “AEGLE, An analytics framework for integrated and personalized healthcare services in Europe” by the EU; H2020 “MEDGENET, Medical Genomics and Epigenomics Network” (No.692298) by the EU; H2020 “CLLassify, Innovative risk assessment for individualizing treatment in chronic lymphocytic leukemia” (No.702714) by the EU; Associazione Italiana per la Ricerca sul Cancro AIRC Investigator grants #20246, and Special Program Molecular Clinical Oncology AIRC 5 per mille #9965; Progetti di Rilevante Interesse Nazionale (PRIN) #2015ZMRFEA, MIUR, Rome ,Italy; TRANSCAN-179 NOVEL JTC 2016; project CEITEC 2020 (LQ1601) by MEYS-CZ, project AZV-MH-CZ 15-30015A-4/2015; JCS was funded by Bloodwise (11052, 12036), the Kay Kendall Leukaemia Fund (873), Cancer Research UK (C34999/A18087, ECMC C24563/A15581), Wessex Medical Research and the Bournemouth Leukaemia Fund; Special Program Molecular Clinical Oncology 5 x 1000 No. 10007, Associazione Italiana per la Ricerca sul Cancro Foundation Milan, Italy; Progetto Ricerca Finalizzata RF-2011-02349712, Ministero della Salute, Rome, Italy.
TM is recipient of a Marie Sklodowska-Curie individual fel- lowship (grant agreement No. 702714), funded by the EU H2020 research and innovation programme.
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